metadata: authors: 0: author: Burns,Andrew
1: author: Jooste,Charl
2: author: Schwerhoff,Gregor
count: Pakistan
docna: 0: docna: Climate Modeling for Macroeconomic Policy :
            A Case Study for Pakistan
docty: Policy Research Working Paper
subtopic: Climate Change and Environment,Climate Change and Health,Science of Climate Change,Adaptation to Climate Change,Energy and Environment,Energy Demand,Energy and Mining,Natural Disasters,Climate Change Mitigation and Green House Gases
teratopic: Health, Nutrition and Population,Science and Technology Development,Energy,Environment
abstracts: cdata!: As the effects of climate change become
            increasingly evident, the design and implementation of
            climate-aware policies have assumed a more central role in
            the macroeconomic policy debate. With this has come an
            increasing recognition of the importance of introducing
            climate into the economic policy making tools used by
            central economic policy making agencies (such as ministries
            of finance and ministries of planning). This paper
            integrates climate outcomes into a macro-structural model
            for Pakistan, the kind of model that is suitable for use on
            a regular basis by ministry staff. The model includes the
            standard set of variables and economic logic that are
            necessary for the kinds of forecasting, economic policy, and
            budgetary planning analysis typically conducted by central
            ministries. In addition to standard outputs (unemployment,
            inflation, gross domestic product growth, and fiscal and
            current accounts), the model generates climate outcomes
            (tons of carbon emitted and economic and health damages due
            to higher temperatures and pollution). These outcomes are
            generated when specific climate policies such as mitigation
            are analyzed, but also when other policies are analyzed that
            might have unanticipated climate impacts. The paper
            describes the changes made to the World Bank’s standard
            macro structural model, MFMod, in integrated climate
            outcomes, climate policies, and the economic impacts of
            climate on Pakistan’s economy. Notably, carbon-tax scenarios
            show that a $20 carbon tax can reduce emissions in Pakistan
            by 36 percent by 2050. Gross domestic product impacts could
            also be positive, if the revenues from the carbon tax were
            used to reduce reliance on heavily distorting taxes. The
            model also quantifies associated co-benefits from reduced
            local air pollution and better health and productivity
            outcomes. In the absence of action to restrain climate
            change, the model suggests that increased temperatures and
            rain variability could reduce output by as much as 10
            percent compared with a scenario where global temperature
            rises were minimized.
geo_reg_key: 517198,517212,517191
repnme: repnme: Climate Modeling for Macroeconomic Policy :
            A Case Study for Pakistan
display_title: Climate Modeling for Macroeconomic
            Policy : A Case Study for Pakistan
listing_relative_url: /research/2021/09/33437191/climate-modeling-macroeconomic-policy-case-study-pakistan
topicv3: Energy and Extractives m1326595 507,Climate Change m1326829 567,Climate Change Mitigation m1327079 579,Climate Change Adaptation m1327767 574,Flood m1503315 3216
publishtoextweb_dt: 2021-09-23T00:00:00Z
dois: 10.1596/1813-9450-9780
historic_topic: Health, Nutrition and Population,Science and Technology Development,Energy,Environment
origu: EFI-MTI-Global Macro and Debt (EMFMD)
no_of_pages: 116
author_keys: 0: author_key: 759200
1: author_key: 1368476
2: author_key: 1688091
model_extractions: Policy Research Working Paper 9780 Climate Modeling for Macroeconomic Policy A Case Study for Pakistan _Andrew Burns Charl Jooste Gregor Schwerhoff_ Macroeconomics, Trade and Investment Global Practice September 2021
Policy Research Working Paper 9780

# **Abstract**

As the effects of climate change become increasingly evident, the design and implementation of climate-aware policies have assumed a more central role in the macroeconomic policy debate. With this has come an increasing recognition of the importance of introducing climate into the economic policy making tools used by central economic policy making agencies (such as ministries of finance and ministries of planning). This paper integrates climate outcomes into a macro-structural model for Pakistan, the kind of model that is suitable for use on a regular basis by ministry staff. The model includes the standard set of variables and economic logic that are necessary for the kinds of forecasting, economic policy, and budgetary planning analysis typically conducted by central ministries. In addition to standard outputs (unemployment, inflation, gross domestic product growth, and fiscal and current accounts), the model generates climate outcomes (tons of carbon emitted and economic and health damages due to higher temperatures and pollution). These outcomes are generated when specific climate policies such as mitigation are analyzed, but also when other policies are analyzed that might have unanticipated climate impacts. The paper describes the changes made to the World Bank’s standard macro structural model, MFMod, in integrated climate outcomes, climate policies, and the economic impacts of climate on Pakistan’s economy. Notably, carbon-tax scenarios show that a $20 carbon tax can reduce emissions in Pakistan by 36 percent by 2050. Gross domestic product impacts could also be positive, if the revenues from the carbon tax were used to reduce reliance on heavily distorting taxes. The model also quantifies associated co-benefits from reduced local air pollution and better health and productivity outcomes. In the absence of action to restrain climate change, the model suggests that increased temperatures and rain variability could reduce output by as much as 10 percent compared with a scenario where global temperature rises were minimized.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aburns@worldbank.org, cjooste@worldbank.org, and gschwerhoff@imf.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Climate Modeling for Macroeconomic Policy: A Case Study for Pakistan<sup>1</sup>

Andrew Burns<sup>♠</sup> Charl Jooste<sup>♣</sup> Gregor Schwerhoff<sup>♦</sup> JEL Classification: C54, E10, Q4, Q54 Keywords: Economic Modeling, Carbon Pricing, Climate Change

> 1 This paper is a product of the MTI Department/GP. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at <u>http://www.worldbank.org/prwp. We thank the Foreign Commonwealth and Development Office (FCDO) for funding this project. The f</u> <mark>indings, interpretations, and conclusions are the authors’ own and should not be attributed to the World Bank, its Executive Board of Directors, any of its member countries</mark> or <mark>the FCDO</mark> . The authors would like to express their thanks to comments and contributions from Zehra Aslam, Simon Black, Enrique Blanco Armas, Florian Blum, Benoit Campagne, Hasan Dudu, Adnan Ghumman, Stephane Hallegatte, Dirk Heine, Janet Minatelli, Yinan Zhang, Paulina Schulz Antipa, Julie Rozenberg, Ted Loch-Temzelides, Florent Johan McIsaac and Muthukumara S. Mani.

> ♠ Andrew Burns is the Global Lead for Macroeconomic Modelling at the World Bank. Email: <u>aburns@worldbank.org</u>

> ♣ Charl Jooste is a Senior Economist and modeler at the World Bank. Email: <u>cjooste@worldbank.org</u>

> ♦ Gregor Schwerhoff was an economist and modeler at the World Bank when the work in this paper was performed.  He has since moved to the IMF and can be reached at: <u>gschwerhoff@imf.org</u>
# 1. Introduction

Avoiding catastrophic climate change by reducing greenhouse gas emissions is a global public good. Left unchecked, climate change will seriously challenge many developing countries. While there is a strong scientific consensus on the threats of climate change and its linkages to economic activity, most developing countries lack the quantitative tools, such as an economic model, to evaluate the economic costs of climate change and the benefits of policies to limit damages. Such a model is needed, if climate change policy and outcomes are to be evaluated on an equal footing with other policy priorities typically addressed by central economic ministries.

The economics of climate change has produced a substantial body of knowledge to date, with the modern literature arguably beginning with the work of William Nordhaus in the late 1970s. Climate economists have illustrated the costs of climate change, in terms of: the increased incidence and seriousness of sudden disasters, the gradual deterioration of production inputs, and the economic costs and benefits of mitigation and adaptation. However, the influence of this work at the individual country level has been limited partly because the scope of analysis has tended to be global, and because much of the analysis has been housed in environment ministries – far from the economic policy-making centers in most economies. And yet, climate change can be expected to generate enormous impacts at the macroeconomic level, in terms of fiscal outcomes, productivity growth, health and economic competitiveness. Furthermore, the linkage between climate change and welfare is much stronger than previously perceived (World Bank 2014), putting climate front and center in policy considerations.

Supported by the _Program for Asia Resilience to Climate Change Trust Fund_ , a trust fund of the Government of the United Kingdom,<sup>2</sup> this paper describes how to integrate climate outcomes and policies into the kinds of economic models typically used by non-specialists working in central ministries. As such it is not seen as a replacement for, but a complement to the more detailed models that might be found at an environment ministry or models used in research. The easy-to-use MFMod framework mirrors in many ways the tools already used by central agencies to perform forecasts, budget projections and policy analysis. Integrating the main economic costs and benefits of climate change and climate policy into this easy-to-use framework will help mainstream climate outcomes and policy into the day-to-day work of Ministries of Finance and Economy, putting climate on the same footing as other policy priorities such as unemployment, inflation, fiscal sustainability, and social policies aimed at reducing poverty.

Such an integrated approach should allow climate’s effects on the economy to be taken into account more rigorously, but also the effect of economic policy on climate (including policies whose climate effects are indirect and unexpected, such as a tax reform that might benefit a carbon intensive sector of the economy).

> 2 The “Program for Asia Resilience to Climate Change Trust Fund” (PARCC) which aims to increase the resilience of countries in Asia to climate change through regional cooperation, innovation and capacity building. PARCC is supported by the FDCO and the UK Met Office to deliver a transformational change in the quality, accessibility and use of weather and climate information at levels of decision-making to support sustainable development in South Asia.

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# 2. Context and background

Despite their significant fiscal and macroeconomic implications, work programs around climate change mitigation and adaptation are poorly integrated into the decision-making process of Pakistan’s Ministries of Finance and Planning. In particular, the impacts of climate change are not measured by the macro-fiscal tools used by policy makers in Pakistan. Neither the State Bank of Pakistan (SBP), nor the Ministry of Finance currently uses macroeconomic forecasting and analysis models that capture the potential impact of climate change on the country’s economy or of the economy on climate. In large part this reflects the underlying complexity and specialized nature of many climate models, that tend to be housed in Environment Ministries or are run by outside consultants at the request of a Ministry.<sup>3</sup> As a result, policy makers lack the tools necessary to quantify the effect of climate policies (or inaction) on economic growth, fiscal sustainability and Pakistan’s broader development agenda.

The work described in this paper addresses this gap by integrating a climate change module into an existing macro-fiscal framework with an easy-to-use interface. The objective is to provide policy makers in Ministries of Finance and Planning with economic models that can be used to plan and analyze the range of economic issues that currently preoccupy them, including fiscal policy (spending and revenues), monetary policy, sectoral activity, external accounts, but which also generate climate outcomes (emissions, economic damages from heat, changed rainfall patterns and pollution) that current tools do not. The model developed maps out the linkages between macroeconomic policy levers, fiscal aggregates and climate outcomes and incorporates a comprehensive set of policy levers covering mitigation measures, co-benefits derived from mitigation, damages caused by climate change and potential adaptation policies.

The work described in this paper focuses on extending an existing macroeconomic modeling framework, which is derived from the World Bank’s Macro-fiscal model (MFMod). MFMod is a macrostructural model (see Burns _et al._ (2019) for a technical description) similar to many models used at central banks and ministries of finance around the world. It is the main model used by the World Bank to produce its biannual Macro Poverty Outlook publication.

The model extensions of MFMod for Pakistan (PAKMod) draw from the existing climate literature to introduce emissions and pollution modules; damage functions from higher temperatures, pollution and flooding on economic activity; and an adaptation module to analyze the economic benefits of adaptation investments to adjust to climate change. Importantly, PAKMod recognizes the pervasive informality in the Pakistan economy. Combined with the pre-existing features of the MFMod framework, PAKMod provides a vehicle for systematically evaluating side-by side the climate impacts, as well as the traditional social and economic impacts (growth, fiscal sustainability, inflation and current account stability) of both climate and non-climate policies. While Pakistan is used as a pilot case, many of these climate features can be added to other country models too.

It is expected that the economic modeling techniques developed in this activity will serve as a blueprint for future applications to other countries.

> 3 That current modeling has room for improvement is expressed by Ghafoor _et al._ (2016): “[C]limate-economic models need to be extended to include a wider range of social and economic impacts. Gaps need to be filled, such as the economic responses of developing countries”.

3
# Damages caused by climate change

Climate change is a serious challenge for Pakistan. According to the Global Climate Risk Index 2019, Pakistan was the country 8<sup>th</sup> most affected in the world by climate-related events between 1998 and 2017, experiencing 145 events over this time period (Kreft et al. 2018).

Pakistan faces three main economic challenges from climate change: increased variability in rain fall leading to water shortages, flooding, and drought; decreased agricultural productivity as temperatures rise; and a decrease in worker productivity as temperatures rise (Young et al. 2019; Asgary, Anjum, and Azimi 2012; Moore and Diaz 2015).<sup>4</sup> Additional types of damages exist, but few data are available (see Section 5).

Agriculture in Pakistan is still the primary source of employment and the primary user of water. Agricultural water use is highly dependent on irrigation in otherwise arid and semi-arid areas through the Indus Basin Irrigation System. Water use in agriculture is inefficient due to the absence of water monitoring and an incomplete water pricing system (World Bank 2018b). With demand from other sectors (municipalities and industry) rapidly growing and current withdrawal levels nearing 60 percent of renewable water supply, misallocation of water resources and resulting supply gaps are already a risk (Young et al. 2019). Climate change is expected to exacerbate this by increasing total water demand by between 16 and 42 percent by 2050 (World Bank, 2019). Water availability is also expected to become more variable, given enhanced glacier melt and more erratic precipitation patterns, which poses risks for the agricultural sector due to limited buffer capacity in the irrigation system.

Agriculture in Pakistan will also be affected as climate change directly impacts land fertility, crop failure rates and livestock productivity.<sup>5</sup> While rising temperatures could lead to minor yield improvements in the north by extending the growing period, the yield of staple crops, such as wheat, maize, or rice, which are predominantly grown in the south, are projected to decrease by up to 20 percent insecurity (compared with a baseline without climate change), resulting in higher food prices and increased food. Livestock production is predicted to decline by 30 percent (relative to baseline), in part because rangelands will become increasingly stressed by longer droughts (UNDP 2017). Salinization of land and aquifers threatens the fertile Indus Delta and sea level rise will aggravate this situation, further contributing to land degradation and loss of fertile soil (Giosan et al. 2014; K. M. Salik et al. 2015).

The third dimension of Pakistan’s vulnerability to climate change is exposure to hydrological and meteorological hazards, including storms, floods, heatwaves and droughts. On average, 3 million people are affected by natural catastrophes every year, with floods being the dominant hazard (77 percent of the people affected), followed by droughts (14 percent). The recurring flood events during 2010-14 alone resulted in monetary losses of over US$18 billion (Government of Pakistan 2016). As the cryosphere (snow and ice cover) in the mountainous areas is retreating and sea levels are rising due to climate change, the risk of flooding is increasing. Other extreme events which are likely to become more prevalent include heatwaves and droughts. For instance, the unprecedented 2015 heatwave in Karachi took the lives of over 1,200 people (MOCC Pakistan 2015).

> 4 Other challenges related to climate change include coastal erosion (Kanwal et al. 2020), and forest management (World Bank 2018a). These are not modeled in this paper.

> 5 For a review of climate change impacts on agriculture, see (UNDP 2017; World Bank 2016).

4
# Policy options to address climate change

Policy options to address climate change can take one of two forms:

**Mitigation policies** seek to reduce Pakistan’s contribution to global and local climate change. Although Pakistan – according to the Carbon Dioxide Information Analysis Center – only contributed around 0.46 percent of global carbon emissions in 2014, mitigation policies that seek to reduce emissions or limit their rise as the economy grows are relevant because of Pakistan’s high emission intensity of GDP. Unless emissions intensity declines more quickly than in the past, Pakistan’s emissions are expected to more than double by 2080 as the country develops **.**<sup>6</sup> While reduced contribution global emissions are a clear benefit, albeit small, mitigation measures can also generate important co-benefits that support the economy.

**Adaptation policies** can help reduce Pakistan’s vulnerability to climate change shocks. Adaptation includes all policies that reduce the economic and social impact of climate degradation. While adaptation is often beneficial at the individual level, meaning that the private benefits will exceed the cost of adaptation, there are many cases in which externalities or market failures imply that private individuals will not undertake the investment, even though at the social level the benefits of adaptation (private and social) would exceed the costs. For example, economies of scale mean that the benefits from a single dyke around a community might exceed the costs of its production and might be much less expensive than many private dykes.

# Existing climate mitigation policy in Pakistan

The energy sector represents 46 percent of emissions in Pakistan with agricultural emissions (principally from livestock) reflecting a substantial 41<sup>7</sup> percent share (Sánchez-Triana et al. 2014; World Bank 2018b). Energy is the fastest growing contributor to GHG emissions, with increasing demand for energy being met primarily through imported natural gas and oil, with some contribution from hydropower (Figure 1).

Three factors drive the negative climate implications of the energy sector. First, Pakistan’s energy mix directly exacerbates GHG emissions, as natural gas losses during transport are high<sup>8</sup> and natural gas releases methane, which, when it is leaked, traps 28-34 times more heat in the atmosphere than an equivalent amount of carbon. Second, significant subsidies result in lower domestic market prices for natural gas than international market prices, thus increasing demand. Third, the non-hydroelectrical portion of Pakistan’s power sector is characterized by a dominance of comparatively inefficient public power plants, whose operational performance is further constrained by underpriced electricity and high transmission and distribution losses. The lack of access to reliable electricity for households and firms imposes significant economic costs, as firms and individuals are forced to rely on inefficient and highemission private generators which contributes to reduce household and firm productivity. Estimates put these costs at around 4.75 percent of GDP (Zhang, 2018).

Reducing emissions requires policy measures that internalize environmental externalities in gas and oil consumption, reduce gas leakage, limit theft of electricity and promote renewable energy use. Under the Paris Agreement Pakistan has highlighted its intention to reduce GHG emissions by 20 percent by 2030, relative to the projected emissions. Prioritized future mitigation pathways to achieve this target include,

> 6 After India, Pakistan produces more GHG emissions per GDP than any other country in the region.

> 7 <u>https://www.climatelinks.org/sites/default/files/asset/document/GHG%20Emissions%20Fact%20Sheet%20Pakistan_6-32016_edited_rev%2008-18-2016.pdf</u>

> 8 Natural gas losses peaked at 14.3 percent in 2015 – compared to 1 to 2 percent in OECD countries.

5
among others, increasing energy grid efficiency, coal generation efficiency and renewable and hydro energy expansion. In addition, Pakistan levies various taxes on fuel – including petrol and gas levies, customs duties on import and sales tax - which have the potential to provide a disincentive for burning fossil fuels. However, according to IMF (2018), there was a gap of USD 18.56bn (6.82 percent of GDP) between the fuel taxes justified by global external costs and the fuel taxes that Pakistan actually levies (only USD 4.8 billion is the global warming externality, the rest are domestic externalities). Most of this tax gap comes from low oil prices (8.41bn), followed by natural gas (5.55bn), coal (1.94bn) and electricity (2.57bn).

Figure 1: Electricity generation by source in Pakistan <!-- Start of picture text --> 70.0% Coal Oil<br>Gas Nuclear<br>60.0% Hydro Other RE<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Source: (IEA 2021)

# Existing efforts to adapt to climate change in Pakistan

The extent of Pakistan’s vulnerability to climate change induced disasters emphasizes the need for adaptation measures, which are guided by the National Disaster Risk Reduction Policy (2013). The policy advocates creating resilient communities with disaster risk reduction interventions at the local level, including the need to build links, information exchange and communication channels between community-based actors, local governments, and District Disasters Management Authorities/Provincial Management Authorities. The Government of Pakistan has also recently passed the Climate Change Act 2017, which establishes the Climate Change Authority and Climate Change Fund to advance the climate change agenda, tasked with contributing toward prevention and mitigation of disaster and climate risks.

# Future mitigation and adaptation options in Pakistan

Going forward, multiple pathways exist for Pakistan to increase its mitigation and adaptation efforts. As concerns mitigation. For one, fuel taxation that equalizes the marginal cost paid by fuel consumers with the internal and external costs generated by burning fuels can play a central role in reducing emissions 6
while generating positive fiscal co-benefits. Currently hydroelectricity, is the dominant source of renewable energy production (Figure 2). However, going forward while there is scope for expansion of hydro power, the bulk of new renewables are destined to come from solar (Table 1 see (Ghafoor et al. 2016)).

Figure 2: Electricity capacity from renewable energy in Pakistan <!-- Start of picture text --> Electricity capacity from renewable energy in Pakistan<br> 14 000<br> 12 000<br> 10 000<br> 8 000<br> 6 000<br> 4 000<br> 2 000<br> 0<br>Wind Solar Bioenergy Hydropower<br><!-- End of picture text --> _Table 1: Capacity and potential for renewable electricity in Pakistan, in MW_

|Source|Capacity in 2019<br>Potential according to Ghafoor et al. (2016)|
|---|---|
|Hydropower|9,900<br>45,000|
|Solar|1,329<br>2.9 Million|
|Bioenergy|432<br>1,012|
|Wind|1,236<br>20,000|

With regards to future adaptation measures, evidence highlights the major potential benefits of developing more productive crops, extending agricultural research and extension, improving irrigation efficiency and encouraging the use of rainfall index insurance (Parry et al. 2014a; Parry, Mylonas, and Vernon 2018). Reducing the economic impact of potential future climate-related disasters (floods, droughts, heat waves) requires investment in the resilience of infrastructure and the development of a robust disaster risk management (Stéphane Hallegatte, Hourcade, and Dumas 2007). For instance, improving hydromet infrastructure (local weather, notably rainfall, forecasting and dissemination) and operationally targeted climate information and early warning systems can help key economic sectors such as agriculture, aviation, water, urban infrastructure, hydropower, and tourism adapt to and overcome climate-induced vulnerabilities.

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# 2.1 Link to other World Bank Projects

The model described in this paper has the potential to inform and support other climate-related work in Pakistan. Recently, the World Bank Islamabad office Pakistan CMU has begun work on a climate change platform to consolidate all climate-related analytical work in Pakistan to extract and disseminate key lessons for policy makers and to streamline their application into project preparation. The model can help develop an integrated approach and assessment of the broader economic effects associated with these efforts. At a regional level, the model can support the planned technical assistance project (“Regional Climate Resilience and Adaptation Program”) aimed at informing national-level investments and programs on climate resilience across South Asia. At the same time, this model will provide a convenient platform for monitoring the climate co-benefits of seemingly un-related policies, such as tax and spending policies that may have important indirect climate impacts.

In addition to the climate change platform and regional work, the proposed model can also support the ongoing dialogue with the Government of Pakistan relating to climate change at the federal and provincial level. This includes the provision of technical assistance to achieve Pakistan’s INDCs under the “Green Growth and Climate Resilient Development” project and a continuous dialogue with the Ministry of Climate Change centered around the impact of climate change on water security and agricultural productivity. Finally, the models will also allow for analysis and simulation of policy options outside the direct realm of climate change, such as the fiscal and economic effects of tax policy changes.

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# 3. Introducing climate considerations into PAKMod

PAKMod is a macrostructural model, with the long run determined by a mixture of calibrated and estimated parameters following a neoclassical framework. The short run is primarily data driven. External and domestic shocks perturb the economy away from equilibrium, with the speed that the economy returns to equilibrium determined econometrically on the basis of the way the economy has adjusted historically. The functional forms of the equations in the model are derived from economic theory where household tend to optimize consumption decisions to maximize utility, and firms minimize costs by adjusting their use of factor inputs. The model is not, however, fully micro-consistent in the way that CGE or DSGE models are. The main strand of climate-macro models can be summarized as:

- **Integrated Assessment Models (IAMS** ): Integrated Assessment Models (IAMs) are large-scale numerical models, which combine an economic analysis with key insights on climate impacts and climate policy. Typically, the models (i) take a global and mitigation perspective, (ii) consider long time horizons of 30 to 100 years into the future and (ii) have a major focus on the energy system.

- **Spreadsheet models:** The IMF has developed a model in an Excel workbook (Parry et al. 2014b; Parry, Mylonas, and Vernon 2018) to evaluate climate policy. The workbook contains large amounts of data on greenhouse gas emissions and a number of co-benefits, including for developing countries. It allows a fast and comprehensive evaluation of the quantitative effects of policies related to climate change. It has been applied to guide G20 countries in terms of actions that will help them converge to their GHG pledges. In general, the framework underscores the efficiency of pricing instruments (e.g., carbon tax) as opposed to other instruments (e.g. energy efficiency systems and fuel taxes) to hit the climate targets. The CPAT (carbon pricing and taxation) model under development by the World Bank and the IMF covers similar ground but also includes pollution, health and productivity effects from GHG production. The framework accounts for the energy system and models the impact of carbon prices on stranded assets too.

- **DSGE models:** Some DSGE models include fossil fuels directly as an input into production and consumption (Hassler and Krusell, 2018). These models include features from the climate economics literature such as a carbon-cycle component in which emissions lead to greenhouse gasses which warms the earth’s atmosphere. The loop is closed via a damage function that relates GDP losses to the amount of carbon in excess of some defined steady state. Damages in these models can either be simulated via changes in TFP or changes in utility. Typically, these models are assumed to return to the steady state growth rate – i.e. damages affect levels but not growth. These models exclude significant nonlinearities or tipping points where economies are structurally altered. That said, nothing hinders the inclusion of tipping points. Lemoine and Traeger (2014) shows that tipping points raises the optimal carbon tax, with the resulting policy aiming to lower optimal peak warming by 0.5 degrees centigrade.  With extreme climate shocks, rebuilding can be delayed, generating hysteretic effects that can alter the steady state (Stéphane Hallegatte, Hourcade, and Dumas 2007).

- **Macro Structural Models:** Macro structural models are the mainstay forecasting and policy tools in many central banks and ministries of finance (Dalsgaard, André, and Richardson 2001; Brayton, Laubach, and Reifschneider 2014; Saxegaard 2017). Like computable general equilibrium (CGE) and DSGE models, these are general equilibrium models that cover the entire

9
macro economy by linking various accounts through a set of identities and behavioral equations. There are relatively few examples of macro structural models being used for climate change policy. Notable exception include the Cambridge Econometric Energy-Environment-Economy model (Cambridge Econometrics 2014), the St. Lucia model of the World Bank and the IMF model described in Parry, Mylonas and Vernon, (2018).

- **Stock-flow Consistent Models:** These models are based on the interactions of the balance sheets of the private sector (banking and non-bank), household, government, external sector and monetary policy sectors. A key difference in these models and other models is that firms and households do not necessarily maximize a utility or profit function (see for example Monasterolo and Raberto (2018). The models have strong Keynesian effects and focus on disequilibrium behavior of firms and households. It is not always possible to model the balance sheet of all the economic actors since data are not readily available for data poor countries. The climate modules in these models are very similar to a DSGE or MS model.

- **Recursive Dynamic Computable General Equilibrium Models (DR-CGE):** Dynamic Recursive CGE models describe the behavior of economic agents with a system of equations.  The economic behavior in DR-CGE models is very tightly linked to standard economic theory. Generally, these models assume that all markets in the analyzed economy are in equilibrium (i.e., observed relative prices clear all markets simultaneously), and implicitly that the observed state of the economy reflects a constrained optimum. These were among the first models to be used to analyze a more granular level than the IAMS the impacts of climate change on economies and of economic policy on climate.

Given data constraints in some equations, several parameters were calibrated using the methodology outlined in Burns and Jooste (2019). Beyond customizations necessary to better capture the structure of the Pakistani economy, five significant features were added to the framework:

1. **A more disaggregated energy sector** was integrated into both the production and consumption sides given the importance of hydrocarbons as a source of greenhouse gas emissions and particulate pollution.

2. **An emissions and pollution module** were added to capture the main channels by which economic activity affects climate outcomes.

3. **Damage functions** were introduced to capture how pollution, flooding and higher temperatures impact economic activity by reducing working time; by reducing labor productivity; and by reducing agricultural productivity.

4. **Adaptation investment functions** were introduced to capture how investments to increase the climate resilience of the economy can reduce the damages that might otherwise occur.

5. **An informal sector** was introduced to ensure that economic behaviors unique to that part of the economy were properly captured by the model.

The following sub-sections describe the adjustments made to the standard MFMod structure to accommodate climate change in PAKMod.

# 3.1 GDP components

As in MFMod, the modeling of GDP comprises three measurements. GDP from the (i) expenditure side, (ii) production side and (iii) income side. Each of these are described below.

10
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<!-- Start of picture text --> Schanae Export competitiveness Schanae Demand for exports<br>20 30<br>PX/PC Real exports<br>10 —— REER 20 — Foreign demand<br>0<br>10<br>-10<br>0<br>-20<br>-30 -10<br>-40 -20<br>98 oo 02 04 06 08 10 12 14 16 18 98 00 02 04 06 08 10 12 14 16 18<br><!-- End of picture text --> =at+é0

|+B A(pxkey )
(P =[o <u>r</u> =m =m
=at+ pm -Le ~~(——)~~

|

~~(—~~ (Y) (Y) () (y
The model uses data from the input-output (IO) table which provides more details about intermediate demand between sectors and the composition of final demand across sectors. The IO table maps the different intermediate products (notably energy projects) used to produce the goods and services that comprise final demand. By using the technical coefficients from this table (how many units of electricity and coal are needed to produce a unit of transportation) and time series data for final demand and aggregate sectors, we are able to generate pseudo time series for the intermediate demands of each sector (assuming that the technology of production is constant over time).

The technical relationships between elements of final demand and sectors of production that are summarized by the IO table are illustrated in the following diagram, which focuses on a single sector i.

Government Households ii,II ii,CC ii ii,ττ ααii,GGGG αα II αα CC VVAA + αα ττ<sup>KKIIDD</sup> Foreign countries Sector i Other sectors ii,XX ii,IIKKDDSS SS αα XX αα IICCTT ii,MM ii,IIKKD DD αα MM αα IICCTT Effectively, one unit of demand for exports will imply units of final demand for the output of sector I, which in turn would generate units of intermediate demand for the output of each sector j. In ii,XX αα this way each component of expenditure GDP is shared out to individual sectors. E.g. the exports of sector ii,XX jj,ii are written as the share of total exports, given by αα ∗ αα . It is important that = 1 , such that all of exports are shared out. Imports are given by . The intermediate goods supplied to and demanded from ii,XX ii ii,XX ii the other sectors is denoted as and , respectively. In addition, sector αα ∑αα supplies government MM consumption , investment goods and consumption goods . Value added ( ) and indirect taxes ( ) SS DD IICCTT IICCTT ii correspond to the value of capital and labor provided by households (or equal to GDP from the income GG II CC VVAA ττ<sup>KKIIDD</sup> side, although without capital and labor data, respectively). The equations for total source (on the left of the equation) and total use (on the right of the equation) with the equilibrium condition of Supply = Demand imposed can be written as, . For the purposes of this model there is no need to include all the intermediate demands generated for ii ii,ττ ii,MM ii,IIKKD DD ii,CC ii,II ii,GG ii,XX ii,IIKKDDSS SS VVAA + αα ττ<sup>KKIIDD</sup> + αα MM+ αα IICCTT = αα CC+ αα II+ αα GG+ αα XX+ αα IICCTT each final demand (the number of variables expands by the number of sectors and for each price, quantity and value equivalents). Instead we focus on the intermediate demands for energy from each sector but lump together all other intermediate demands. As is customary in CGE models, we assume a Leontief technology (fixed ratio) for intermediate demands, which implies that the share of intermediate supply to aggregate supply is constant over the policy and forecast horizons. We define these ratios as: 21
<u>M)</u> <u>(a+yd(@</u> <u>X)-a</u> Aln(Y )=6[mn(Y )-—M(VA )]+BAIn(VA )+e
# _3.2.1.1 Available data on quantities and prices_

Energy related data are sourced from the IEA database “World energy balances”, which is available from the OECD (https://www.oecd-ilibrary.org/).<sup>12</sup> In the model, we aggregate energy into four categories: coal (15.2% of the energy mix in 2017), oil (40.5%), natural gas (36.9%), and “renewables” (7.5%). The “renewables” category includes nuclear power, hydro, solar and wind. While nuclear power is not renewable, all these energy sources have in common that their net carbon emissions are zero or very low. A carbon price would thus not directly affect this energy category. In addition to the 55,561 kilotons of oil equivalent (ktoe) used from the energy sector, Pakistani households also used 30,187 ktoe from biofuels in 2011. Biofuels data are not included in the model due to a lack of macroeconomic data. From a carbon accounting point of view the omission is not serious as biomass is a “carbon neutral” energy source when managed well (Favero, Daigneault, and Sohngen 2020), although it can make a significant contribution to local air pollution. Biomass first absorbs carbon from the atmosphere (when a tree grows, for example) and then releases it again (when wood is burned).

The IEA provides data on energy produced, imported and exported and the shares of fuel type in electricity generation. The data are available in kilotons of oil equivalent (ktoe). While Pakistan exports some oil products, it is a net importer in every energy category, so that we bundle exports and imports as “net imports”. We denote energy produced by Pakistan (in ktoe) as with indicating the category (coal, oil, gas, renewable). Net imports are denoted as , while energy commodities consumed in the production kk tt E E QQ kk of electricity is denoted as . MM kk,tt QQ Historical market price data for energy is sourced from the World Bank’s commodity sheets (pink sheets). kk,tt QQ These are denoted as . The units of the prices are USD per metric ton (USD/mt) for coal, USD per barrel (USD/BBL) for oil and USD per one million British thermal units (USD/MMBTU). The following UUSSDD kk,tt PP conversions are applied to make the units between prices and quantities compatible: = 1.429 ~~,~~ B s sstt = 7.4 and = 39.683 . The International Renewable Energy Agency (IRENA) provides ββ<sup>EECCbbEE</sup> ttCCEE LL bbttss data on renewable energy. Given that historically the largest energy source in the “renewable” category ββ<sup>CCiiEE</sup> ttCCEE ββ<sup>EEbbff</sup> ttCCEE is hydropower, we use the price of hydropower as a proxy for the category historically.<sup>13</sup> For local currency conversion we use the rupee- U.S. dollar exchange rate, . Electricity prices are usually denoted as cents per Kwh, however, for consistency here they are scaled to reflect the price per ton of oil equivalent. tt ffxx In recent years, fossil fuels have been sold below their respective world market prices in Pakistan. While there are plans to reduce or abolish subsidies, fuel subsidies still amounted to a total of 3.4 USD billion in 2017 or 1.14% of GDP according to IEA data. In the model, subsidies on fuel are represented as negative carbon taxes. In the modeling, it is assumed that the government is not transferring funds to energy producers, and hence it is not a direct expenditure but rather a forgone revenue that it would have collected. The fiscal accounting from a debt and overall balance perspective would have been equivalent if it was accounted for as an expenditure. Mitigation policy can take the form of increasing carbon taxes, from the current negative values to zero and then to positive values. Carbon taxes ( ) are expressed as USD per ton of CO2 (USD/t). As indicated by the subscript , carbon taxes are time-variant. The distinction kk tt ττ SS

> 12 All data used in this model can be traced to the original data through the Excel workbook “Pakistan energy and emission data.xlsx”.

> 13 The Pakistani government provides detailed data on renewable energy prices in the “Indicative Generation Capacity Expansion Plan”. We are planning to update the model with these data.

23
of energy type is necessary since the subsidies vary across energy sources. Furthermore, electricity subsidies are netted out of carbon subsidies. kk To calculate the fossil fuel subsidy (or negative carbon tax) per energy source, we use IEA data (for the period 2010 to 2019) and IMF data (for 2007 to 2009) on the total amount of subsidies paid for each of the three energy sources and electricity. Subsidies for electricity are described in more detail below. We use data on the total amount of emissions from each fuel type. Fossil fuel subsidies for energy type divided by total emissions from energy type provides the subsidies for energy type per ton of CO2. The kk data for 1980 to 2006 are calculated using the average share of subsidies in the world market price. kk ii The taxed quantities are the energy sources in ktoe. In order to obtain the tax per ktoe we need a conversion coefficient, denoted as . It will be in units of tons of CO2 per kiloton of oil equivalent (t/ktoe). Following (Carrara and Marangoni 2017), we obtain the following values: = 2.26 , = αα<sup>kk</sup> tt 1.75 , = 1.34 and = 0 . αα<sup>EECCbbEE</sup> kkttCCEE αα<sup>CCiiEE</sup> tt tt tt kkttCCEE αα<sup>EEbbff</sup> kkttCCEE αα<sup>rrEECC</sup> kkttCCEE

# _3.2.1.2 Constructing the energy variables used in PAKMod_

Because we want to track the carbon and other GHG emissions associated with energy production from different sources, the model needs to account for energy volumes both in terms of physical quantities as well as the more traditional national accounting volumes derived from base-year values and market or factor-price deflators. We construct the national account equivalent variables in the model using the data above.

Energy prices and the valuation of energy value added is complicated by the mechanisms by which fuels are subsidized in Pakistan. Historically, crude oil and natural gas are purchased by the government in international markets at market prices ( ), and then sold to firms at a lower fixed cost. The final domestic market price at which firms are allowed to sell products is also fixed by the government. This UUSSDD kk,tt PP implicit subsidy to consumers is represented in the model as a negative carbon tax. In the model, the nominal market value of each fuel type produced is given by �. CCKK UUSSDD kk kk kk where is the amount paid by the firm when purchasing the energy source , and kk,tt kk,tt tt kk,tt tt represents the physical quantity produced (and sold) in kilotons of oil equivalent (ktoe). The remainder of the expression CCKK YY<sup>�</sup> = QQ ∗FFXX �PP ∗ββ + ττ ∗αα kk,tt kk,tt YY<sup>�</sup> QQ reflects the market price of energy in local currency and is the product of the exchange rate and the international market price in USD in barrels, or cubic meters multiplied by a conversion factor (which converts the USD price per commodity unit (say Barrel) to a USD price per ktoe). The final expression ββ<sup>kk</sup> reduces (or increases) the international price by the amount of the implicit subsidy (or carbon tax). Where the carbon tax /subsidy rate ( ) is in USD per ktoe of CO2. where converts the commodity quantity into ktoe of carbon. kk kk tt ττ αα 24
- ». ?

<u>(FX</u>
The production by energy types does not have an exact correspondence in the IO table. The link is thus created by mapping the energy types to the energy sector.

The IO contains four energy related product categories, as listed in the table below.

|**Sector**|**Sub-sector**|**Product**|
|---|---|---|
|**Manufacturing**|Mining|Coal and Lignite; and Peat|
|**Manufacturing**|Mining|Crude Petroleum and Natural Gas (Distribution through Main)|
|**Manufacturing**|Manufacturing|Electricity, Town Gas, Steam, and Hot Water and Natural Water|
|**Manufacturing**|Manufacturing|Coke Oven Products; Refined Petroleum Products; and Nuclear<br>Fuel|

The supply and use tables show that the two mining sectors sell their output mostly to the refining sector. We thus consider the bottom two sectors from the table as the energy sector for the purpose of this project. Total value added will thus be given by K K K K K , Which correspond to agriculture (AGR), services (SRV), non-energy manufacturing (MNF(Oth)), and FFCCSSDD VVGGDD,tt SSDDVVtt IIKKDD,tt EE,tt YY = VVAA= YY + YY + YY + YY Which correspond to agriculture (AGR), services (SRV), non-energy manufacturing (MNF(Oth)), and “energy” (ENE). The energy sector uses fossil fuels and renewables so that we obtain the following nested CES structure.

<!-- Start of picture text --> VA<br>Energy sector<br>AGR SRV MNF (Oth) ENE<br>Coal Oil Gas Ren.<br><!-- End of picture text --> 26
(P 7 = M

-)im

M
-Yp

) + B In(n ) ~~_~~ dD .

yln(w ) ~~_~~ ~~|~~
<!-- Start of picture text --> (4)<br><!-- End of picture text --> (Ye) @) ) =c+6[p ]+yAp )+p ~~(—-~~ 1)+e
|

wo ~~ee~~ ain(y ) = In( )+o in(y )-o i ~~n(—)~~ = n(v )]+6 n ~~(—)~~ In(Y )+e
# 3.3 The labor market

As much as 90 percent of the labor force in Pakistan operates in the informal sector (defined as individuals without formal employment contracts and without access to social security).

Firms are modeled as making the choice to engage in a formal or an informal contract after weighing the advantages and disadvantages of each employment type. Major benefits of the formal sector are access to financial services and government services. A major benefit of being in the informal sector is that firms can avoid direct taxation by the government.

This firm’s decision is captured in heterogeneous firm models as presented in Ulyssea (2018), where firms differ by productivity. Very productive firms tend to be larger than less productive firms and thus benefit from returns to scale. More productive and larger firms, therefore, tend to enter the formal market while less productive firms produce informally. In Appendix 1A, we present a simplified version of the model in Ulyssea (2018). This theoretical result has been supported by several empirical papers finding that reducing labor taxes increases formal employment (Rocha, Ulyssea, and Rachter 2018; Bosch and EstebanPretel 2012; Ulyssea 2010).

Integrating the model from Appendix 1A into PAKMod would be a conceptually convincing solution. However, any calibration would be very uncertain, and would require firm-level data on the distribution of firm sizes (both formal and informal).

Therefore, we follow Ahmed (2012) and assume that informal and formal labor can be combined in a 1 constant elasticity of substitution (CES) function to total labor, � . σσ FF<sup>σσ</sup> II II<sup>σσ</sup> 3.3.1 Data creation tt tt tt CC = �ββ<sup>FF</sup> CC + ββ CC The International Labour Organization provides data for the share of formal employment in total employment (ILO 2020b). This variable, denoted as , allows calculating the historical values for formal and informal employment, KK,II tt ss , FF KK,II tt tt tt CC = �1 −ss �∗CC, II KK,II where total employment, , is available in MFMod. CCtt = sstt ∗CCtt The budget constraint is given as CCtt , where is aggregate wealth, is the net wage paid for formal labor and is the wage paid for informal labor. The first order condition KK FF FF II II FF tt tt tt tt tt tt tt tt tt for the informal sector can then be written as WW CC = (1 + ττII )WW CC + WW CC WW WW tt WW . σσ �<sup>CCtt</sup> ~~�~~ II II tt II tt WW = ββ WW Based on the estimates in Table 1 of Ahmed (2012), we assume CCtt = 0.29 , = 0.71 and 2. ββ<sup>FF</sup> ββ<sup>II</sup> = 1 −ββ<sup>FF</sup> σσ= Available data for Pakistan, do not separate out direct taxes for labor and capital, so that we need to assume that the same tax rate applies to both labor and capital. The effective direct tax rate is thus 31
<!-- Start of picture text --> (— (-)<br>lw ~(1—p)In (—)| +Ap + (1-p)AIn (—)<br>fo m<br>* (os. p )+ pain (— —£<br>« [w 1~p)n (—) ] +<br>-|(osap )+ A (—) |-«<br><!-- End of picture text -->

[2 <u>i</u> ~~(——_}~~
Total wages are then given as The wage bill is given as The share of informal labor in GDP is The representative agent determines her consumption based on income, prices and preferences and allocates the remainder to savings or borrows in the case where expenditures exceed incomes. Private sector borrowing may be derived either from domestic or external sources (capital inflows).

3.5 Fiscal accounts 33
The tax base for direct taxes is the sum of the formal private and public sector wage bill and the gross operating surplus. The tax base for VAT is nominal household and government consumption while nominal imports are the tax base for customs revenues.

3.5.3 Expenditures Government expenditures ) equal the sum of use of goods and services ) , compensation of S employees ) , net acquisition of non-financial assets DD ) , social expenditures GGSS ) (e.g. transfers tt tt of the government to households), interest expenses CCSSEE (GG )II and others )(GG : CC tt tt tt (GG (GG (GG DDSSCC SSDDOO S tt tt (GG (GG DD GGSS CCSSEE II CC DDSSCC SSDDOO Expenditures are mostly discretionary since governments have full control over them. From a modeling tt tt tt tt tt tt tt GG = GG + GG + GG + GG + GG + GG perspective the choice either entails assuming explicit functional forms for expenditures or leaving them as exogenous. We follow the former by specifying a simple rule (which can be switched off in simulation mode). We assume that the government runs a structural deficit target equal to B ∗ , which is assumed to equal -3%. Given that revenues are already determined, each expenditure component (except for tt interest payments) can be modeled as: −B ∗)] ii ii DDSSCC where measures the rigidity of expenditure tt ii tt−1 and ii ii is the mean historical share of expenditure tt tt tt to GG = αα GG + (1 −αα ) [θθ (TT −GG revenues net of debt service costs where = 1 . This ensures that the budget solves to the targeted ii ii αα ii θθ ii budget in the long run. While Pakistan does not follow a fiscal rule, the above functional form does fit the ii ∑θθ reasonably well (Figure 5).

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Figure 5: Fitted values of the fiscal rules <!-- Start of picture text --> 140% 60%<br>120% 50%<br>100% 40%<br>80% Capital Capital_Fitted 30% GNFS GNFS_Fitted<br>60% 20%<br>40% 10%<br>20% 0%<br>1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br>0% -10%<br>1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br>-20% -20%<br>-40% -30%<br>45%<br>40%<br>35%<br>30% Transfers Transfers_Fitted<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>% growth (y/y) % growth (y/y)<br>% growth (y/y)<br><!-- End of picture text --> <!-- Start of picture text --> Source: Own calculations<br><!-- End of picture text --> The user has the option of turning the rule on, useful ensure fiscal sustainability, or to turn off the rule to allow the fiscal consequences under unchanged policies to be examined.

Historically purchases of goods and services and compensation of employees make up the largest share of total expenditure with a large component dedicated to servicing debt (see Figure 6).

35
<!-- Start of picture text --> « Shares of expenditure to total expenditure « Government (%GDP) «<br>120 Wi Capita! 9) Goods andsenices -1 = 90<br>Hh other Qj interest Budget deficit<br>100 -2 —— Govemment debt [RHS} 85<br>80 =<br>TUTTE& E 4ee :75<br>60 5 70<br>40 -6 65<br>7 60<br>20<br>0 | a i 9 50<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

—T7 —_— ~~-~~ i
We do not model the different yields given data limitations on when bonds have been issued, their maturity structure and the coupon rates. Instead, we calculate the historic interest rate on government debt as the share of debt interest repayments to the previous stock of debt. =<sup>GGtt</sup> DDSSCC BB tt Our simplified equation for interest on newly issued debt is a function of the short-term interest rate and ii tt−1 DD a premium ( ): tt SSpprrmm 1 BB BB MMPP MMPP The premium is modeled as an increasing function of debt above a threshold ( tt tt−1 tt−1 tt tt ) (here assumed to be Δii = αα+ θθ(ii −ii ) + ββ Δii + ΔSSpprrmm 60% of GDP): DD<sup>∗</sup> −1�, This expression is important to ensure that Pakistan cannot run infinitely large public debt. When debt SSpprrmmtt = αα+ ββ∗�pp<sup>DDtt−DD∗</sup> This expression is important to ensure that Pakistan cannot run infinitely large public debt. When debt exceeds the threshold, the premium increases the bond interest rates. The higher bond interest rates increase the cost of capital, hence reducing investment.

We can map the nominal interest rates to real rates via the Fisher equation where real rates enter the household decision: The yield curve is then the difference between the long and short-run interest rates: yyiiE II BB SSDD To derive the natural rate of interest we start with the no-arbitrage condition, where the marginal product rrtt = rrtt −rrtt To derive the natural rate of interest we start with the no-arbitrage condition, where the marginal product of capital should equal the cost of capital in steady state.: 37
~~(__)-a~~ -» ~~- Peo]~~
(Q )ea (Yeu)
the health benefits from reduced air pollution have been found to outweigh the cost of climate change mitigation in India (Markandya et al. 2009) and globally (Markandya et al. 2018).

With respect to local air pollutants, in PAKMod we concentrate on the most important air pollutant, particulate matter (PM 2.5). IIASA’s GAINS model, provides a much more detailed modeling of air pollution (including from non-hydrocarbon sources), although without the fiscal and economic levers of the present model.<sup>15</sup>

# 3.7.3 Fuels and pollution level

CO2 stays in the atmosphere for a long time and is decomposed only slowly over time. PM 2.5 by contrast does not accumulate in the atmosphere, because it settles to the ground a short time after emission. The concentration of PM 2.5 in the atmosphere thus strongly depends on recent emissions. Its harmfulness depends on wind patterns, the distance between emission sources and air quality within airsheds.<sup>16</sup> For example, it makes a difference, whether fuels are combusted in power plants, transportation, crop burning, construction or in waste burning.

While the CO2 emissions per fuel is a fuel-specific but global constant, such a constant does not exist for local air pollution. We thus use the data from the Carbon Pricing Assessment Tool (CPAT), see (World Bank 2020b), which calculates country-specific values for the pollutant intensity of fuel combustion from very detailed data. Note that NOx and SO2 are precursors of PM2.5, that means NOx and SO2 are emitted when fuels are burned and then they become PM2.5 in the air (Fantke et al. 2017; Humbert et al. 2011). These data distinguish, for example for five different types of liquid fuels, between emissions in different sectors and whether pollutants are emitted at ground level or from stacks.

Table 2: Fuel consumption and pollution intensity in Pakistan

|**Fuel**|**Fuel consumption in 2017 (ktoe)**|**Pollution (ug/m3 per ktoe)**|
|---|---|---|
|**Coal**|10,079|5.35E-05|
|**Natural gas**|26,155|2.96E-05|
|**Oil**|7,213|2.82E-04|
|**Gasoline**|8,037|3.31E-04|
|**Diesel**|9,539|4.38E-04|
|**LPG & kerosene**|1,508|3.05E-04|
|**Jet fuel**|115|2.93E-06|

Source: for fuels: : (IEA 2021), for pollution: (World Bank 2020b) Table 2 shows the fuel consumption for 2017 and the pollution intensity for Pakistan according to CPAT. We use the pollution intensity for coal and gas directly from the table. For the liquid fuels we take a weighted average, 3.53E-04 ug/m3 per ktoe. This is assigned to oil, which represents liquid fuels in the model. The concentration of PM 2.5 pollution in Pakistan due to the domestic burning of fossil fuels in the year is be calculated as SS

> 15 <u>https://iiasa.ac.at/web/home/research/researchPrograms/air/GAINS.html</u> 16 Air pollution can be transported over long distances due to wind and cloud chemistry.

40
<u>μy μy μy</u> = C −5 <u>m m m</u> PPMM2.5 PPVVKK,tt PPVVKK,tt<sup>3</sup> PPVVKK,tt<sup>3</sup> PPVVKK,tt<sup>3</sup> where CCC , SSll ∗5.35XX and are coal, oil and gas consumed in Pakistan in year + GGSSss ∗2.96XX<sup>−5</sup> + PPiill ∗3.53XX<sup>−5</sup> , measured in kkSSCCpp kkSSCCpp kkSSCCpp ktoe. Notice that the total PM 2.5 pollution concentration in the country can be higher than tracked in the PPVVKK,tt PPVVKK,tt PPVVKK,tt SSll GGSSss PPiill SS model due to additional emissions from natural sources, industrial processes other than combustion and pollution from foreign sources. Further, pollution concentration varies considerably within the country. The country-wide aggregation adopted from CPAT is a simplification, but a necessary one for a country model.

# 3.8 Damage module

Potential GDP is the supply potential of the economy and anchors the real side of the model. It determines how much output can be produced when all of the resource in the economy are fully employed (given existing distortions, technology and preferences). In PAKMod, the standard CobbDouglas specification for potential GDP ∗) is modified along five dimensions to accommodate the climate focus of the model: tt (YY

1. Energy is included as a factor of production (see Hassler, Krussel and Olovsson, 2012);

and the production function is modified to account for damages from climate change, including: 2. Reductions in Aggregate TFP due to lower agricultural productivity 3. Reduction in labor productivity and supply due to higher temperatures 4. The impact of pollution on the labor force and 5. The impact of flooding on capital stock.

Before accounting for damages the standard MFMod production function is modified to include energy as a factor of production. In the revised form potential GDP is given as below, as a function of total factor K ∗ productivity (TFP or ) ), capital stock ) , energy � and structural employment ) . tt tt EE,tt tt (AA (KK �YY (CC ∗ K αα ββ γγ Where is the income share of capital, YY is labor’s share in total income and tt = AAttKKtt CCtt YYEE,tt is the share of energy, where the values of each of these parameters is drawn from the input-output table for Pakistan and set ∗ αα ββ γγ to 0.2, 0.7, and 0.1, respectively. represents total factor productivity, is productive capital and the equilibrium employment level and is determined as: tt tt tt AA KK CC ∗ ∗ P 1564 ∗ is the equilibrium unemployment rate, derived from a NAWRU (non-accelerating wage rate of tt tt tt tt CC = (1 −DDCCRR )PPRR PP unemployment) specification, is the participation rate while P 1564 is the working age population, tt DDCCRR which (like the total population) is exogenous and taken from the UN Population forecasts. The structural tt tt PPRR PP 41
<!-- Start of picture text --> x Gaps Schanae Prices<br>4 50<br>Output gap Marginal cost<br>3 — Unemploymentgap 40 — Producer price<br>2<br>30<br>1<br>0 20<br>-1 10<br>-2<br>30<br>4-10<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->
«(1 . 46*C )).

~~—~~

* (Cc - tmrel).
The population in Pakistan in year , is an exogenous variable derived from UN population projections, while pollution concentration, , (expressed as <u>μE</u> ~~)~~ is derived endogenously as a PPVVKK,tt SS PPCCpp PPMM2.5 function of economic activity (see below). The theoretical minimum risk exposure level, , for PM 2.5 PPVVKK,tt CC ss<sup>3</sup> 5.9+2.4 is assumed to be uniformly distributed between 2.4 and 5.9 so that = 4.15 (GBD 2017 2 SSmmrrppll Risk Factor Collaborators 2018, Supplementary Appendix, page 73). We use <u>μE</u> p SSmmrrppll= as a parameter and calibrated to equal based on data from the year 2017. ddppS hss p rr ss3 rr p rrssCCmm The parameter value is calculated as follows. First Estimates of urban and rural deaths from air pollution . 80XX−06 for Pakistan were extracted from (World Bank 2020b) as = 56,205 and = 34,078 . These were combined with WHO (1987) estimates for average rrssrrbbEE pollution concentrations in urban ( = 56 ) and rural areas ( SSCCS ll AAPP ddppS hss = 52 ) . These were ssrrb CC SSCCS ll AAPP ddppS hss then combined with population data to derive deaths per unit of PM2.5 as ssrrb CC rrssrrbbEE PPMM2.5 PPMM2.5 <u>m3 m3</u> p ~~,~~<sup><u>yy</u></sup> uurruuSSmm ddppS hss p rr<sup><u>μ</u></sup> + rruurrSSll ddppS hss p rr<sup><u>μ</u></sup> The economic value of life is not calculated in the model. Instead we only report deaths related to ddppS hss p rr mm3<sup>μμyy</sup> rr p rrssCCmm= PPCCppPPVVKK,2017 The economic value of life is not calculated in the model. Instead we only report deaths related to pollution.

# 3.8.3.1 Health expenditure attributable to pollution

Health expenditure due to pollution was estimated following the methodologies described in Preker _et al._ (2016); WHO (2020) and elaborated in World Bank (2020). Note that in the model medical costs are not a deadweight loss as the correspond to a redistribution of resources from households to the health care sector.

The first step uses WHO (2020) estimates of total health care expenditure as share of GDP in 2017 (for which data is available), which was 2.9% ( ) or 762.8m rupees. Figure 8: Health care spending by payee as a percent of GDP. TTDDXXPPVVKK,2017 <!-- Start of picture text --> Health Care Expenditure<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>Government Voluntary Out-of Pocket<br>% GDP<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: WHO (2020) 44
The second step determines the share of health expenditure attributable to pollution. For this step data on the disability adjusted life years (DALY),<sup>17</sup> for each disease “d” associated with air pollution (chronic obstructive pulmonary disease, lower respiratory infections, stroke, diabetes mellitus type 2, ischemic heart disease, tracheal, bronchus, and lung cancer), is divided by total disability adjusted life years in each year and then multiplied by the proportion of the population with disease d. � dd,cc,2017 DDAAWWYYss cc,2017 In the model nominal spending on health care is assumed to rise linearly in line with GDP and the dd TTCCS llDDAAWWYYss In the model nominal spending on health care is assumed to rise linearly in line with GDP and the increase in the concentration of 2.5mm particles in excess of the previously discussed threshold level.

## Thus, health expenditure due to pollution in year t is given by

The 2017 HEAP value is calculated as <u>, ,2017</u> . ∗� II EE DDAAWWYYss PPVVKK,2017 PPVVKK,2017 PPVVKK,2017 II,2017 DDXXAAPP = GGDDPP ∗TTDDXX EE,2017 ∗PPAAFF are disability adjusted life years, for disease d, country c, year t and II TTCCS llDDAAWWYYss = . (population attributable fraction) represents the share of all people suffering II,EE,tt EE,2017 DDAAWWYYss TTCCS llDDAAWWYYss from the disease in year , that contracted it because of pollution.<sup>18</sup> Data for the PAF are sourced dd II,EE,2017 II,tt ∑DDAAWWYYss PPAAFF from the CPAT model (World Bank 2020b). dd SS

# 3.8.3.2 Mapping working days lost to economic outcomes

Pollution impacts the economy on the one hand by reducing the labor force due to pollution-related deaths, and reducing total labor supply (measured in hours worked) by the hours lost by those workers who suffer from pollution related disease but do not die.

> 17 The disability-adjusted life year, , is a summary measure which combines time lost through premature death and time lived in states of less than optimal health or “disability”. DALY, for a specific cause , sex , age and year is defined as: DDAAWWYY cc ss SS ,SS EE,ff,bb,tt EE,ff,bb,tt EE,ff,bb,tt where YLL are years of life lost and YLD are years lived with disease. The values for DDAAWWYY = YYW + YYWWDD , and are obtained from the GBD results tool (IHME 2018). DDAAWWYYss YYW YYWWDD

> 18 The population attributable fraction ( ), is the reduction in incidence that would be observed if the population were entirely unexposed, compared with its current exposure pattern. for each mortality type and age range is calculated as PPAAFF PPAAFF = ∑ ∑CCii=1 ii,bb∗�R∗cc <u>�R</u> EE,bb<sup>−1</sup> + 1<sup><u>�</u></sup> ~~,~~ SS EE,bb CC PP PPAAFF ii=1 ii,bb EE,bb<sup>−1�</sup> where R PP EE,bb<sup>is the relative risk for causeccand age groupSSandPP</sup> ii<sup>is the percentage of the population exposed to level of pollutionii.</sup>

45
_Reduction in effective labor supply due to reduced hours worked_ As discussed above labor force ( ) in Pakistan depends on the participation rate ( ) and the working age population ( 15+ ). tt tt WWFF PPRR 1564 PPCCpptt ∗P , tt tt tt WWFF = PPRR PP To account for reduced working days we introduce the notion of effective labor force and substitute this notion for the labor force in the standard MFMod system, where the effective labor force, � is equal to the product of the normal labor force and the ratio of days of work lost to pollution/ divided by total tt WWFF working days (assumed to be 240). ∗�1 −<sup>WWDDWWggCCE</sup> �.<sup>ssttiiC,tt</sup> tt tt The equation above will modify structural employment and hence potential GDP: WWFF� = WWFF tt WWDD _Reduction in effective labor supply due to premature death_ Calculating the macroeconomic impact of a death due to air pollution is more difficult to quantify. World Bank (2020c) provides an age distribution of deaths due to air pollution. With this, we could estimate the number of working years lost due to workers dying prematurely from air pollution. This could then be used to estimate the reduction in GDP.

We could consider the effect of death due to air pollution on GDP per capita. Note that a death reduces not only labor supply, but also the denominator, population size. The death of a retired person would increase GDP per capita. According to World Bank (2020), 57.7% of deaths due to air pollution occur in persons older than 60 years. This means that air pollution deaths can be expected to increase GDP per capita. Notice that this difficulty does not occur for working days lost as sickness caused by air pollution does affect labor supply, and hence GDP, but not the population size. We report deaths due to air pollution separately.

_Fiscal implications of pollution-related disease_ Data show that about 60% of health care expenditure in Pakistan is financed out of pocket while the remainder is effectively financed from the government. In the modeling we assume that these shares are constant so that government’s share of health care expenditures is given by where Θ is the share of health care expenditures financed by households and is set to 0.6. and this PPVVKK,tt (1 −Θ)DDXXAAPP amount is included in the government goods and services expenditure equation.

# 3.8.4 Damages to agricultural productivity

While Pakistan’s mitigation impacts on agricultural damages are negligible, the costs of these given damages are not necessarily small. There is a vast literature on the impact of climate change on agricultural productivity. In an influential study, Schlenker and Roberts (2009) show a strong nonlinearity 46
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similar in the two SSPs. Given current mitigation efforts we choose to work with SSP 5 (“Taking the Highway”). With these scenario choices, we obtain Table 4.

Table 4: Agricultural losses in South Asia

||Net<br>impact|**RCP 2.6**<br>Direct<br>impact|Private<br>Adaptation<br>effect|Net<br>impact|**RCP 8.5**<br>Direct<br>impact|Private<br>Adaptation<br>effect|
|---|---|---|---|---|---|---|
|**2000**|0.00%|0.00%|0.00%|0.00%|0.00%|0.00%|
|**2010**|-1.26%|-2.29%|1.03%|-0.77%|-1.46%|0.69%|
|**2030**|-1.73%|-3.68%|1.95%|-2.29%|-5.25%|2.96%|
|**2050**|-1.17%|-2.30%|1.13%|-4.93%|-10.08%|5.15%|
|**2080**|-1.74%|-3.35%|1.61%|-14.73%|-22.66%|7.93%|

Source: (Havlík et al. 2015) In the model, we will work with the numbers for the net impact, because the private adaptation is reported in the table is effectively the kind of optimization behavior embedded in the model. For the intermediate years, we interpolate linearly.

_Model modifications_ In order to account for climate damages ) to agricultural output (note that it has a negative sign), we include it explicitly in the calculation of value added in the agricultural sector: tt (DD K K K K 1 ) This equation derives value added for the crop sector, a sub-sector of agriculture, from the value in the Δ log (YYVVGGDD,tt) = cc −θθ<sup>VVGGDD</sup> ∗log (YYVVGGDD,tt−1) −log ((1 + DDtt−1) ∗YY<sup>�</sup> VVGGDD,tt−1)) + Δ log ((1 + DDtt ) ∗YY<sup>�</sup> VVGGDD,tt social accounting matrix (SAM). reflects the damages caused by climate change in year . Potential GDP is not modeled bottom up from sectoral values. Instead potential GDP is calculated from DDtt SS the aggregate production factors. The agricultural damage has the effect of reducing TFP, because the same amount of capital and labor produces a reduced amount of output compared to previous years. The modification to potential GDP ∗ is tt ∗ ∗<sup>αα</sup> K YY YY ββ γγ where is the weight of agriculture in total value-added. It would have been ideal to map damages YYtt = (1 + DD∗ωωVVGGDD)AAttCCtt KKtt−1YYEE,tt ∗ ∗ K where is the weight of agriculture in total value-added. It would have been ideal to map damages in the agricultural sector to changes in informal employment and wages, since they are most likely to be YY VVGGDD affected. ωω

# 3.8.5 Heat impacts on labor productivity

There is extensive evidence that labor productivity is sensitive to temperature and that most countries are beyond the optimum, so that further temperature increases will reduce labor productivity (Burke, Hsiang, and Miguel 2015). According to Sherwood and Huber (2010 and Buzan and Huber (2020), there is an upper limit for humans to adapt to higher temperatures and at strong climate change many world regions would become uninhabitable. Firm level studies in India (Somanathan et al. 2018; Adhvaryuy, Kalaz, and Nyshadham 2019) and China (Zhang et al. 2018) show that heat reduces worker productivity.

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productivity under RCP 2.6 and 8.5 respectively. Years between the dates in the table are estimated via linear interpolation.

Table 5: Labor productivity lost due to heat in RCP 8.5, in percent

||**Work ho**|**urs lost**|**Change as compared with 2015**|
|---|---|---|---|
|**Year**|RCP 2.6|RCP 8.5<sup>22</sup>|RCP2.6<br>RCP8.5|
|**1995**||3.73|-0.67<br>0.67|
|**2015**||4.1-4.7|0<br>0|
|**2025**||5.22|0<br>.82|
|**2055**||7.00|.92<br>2.6|
|**2085**||9.97|0.82<br>5.57|

These estimates give a good indication for the order of magnitude of heat stress on labor productivity, but do not include other factors like how heat will interact with air pollution, and changes in demographics (see below).

## _Model modifications_

Potential GDP is modified once more, but now accounting for labor productivity losses due to heat. In order to capture the loss in labor productivity, we distinguish between work hours and effective work hours , where h is the percentage loss of hours given in the second data column in Table 5. WW We implement this by modifying the equation for potential GDP to WW<sup>�</sup> = (1 −h)WW ∗ ∗ K �(1 −h � , ∗ YY αα ββ γγ where is trend TFP, is structural employment and YYtt = (1 + DD∗ωωVVGGDD)AAtt tt)CC is the share of agriculture in GDP. The first tt KKtt−1YYEE,tt part, , was discussed above in the agricultural damages section. YY tt tt VVGGDD AA CC ωω YY VVGGDD 1 + DD∗ωω

# 3.8.6 Damages to physical capital from flooding

Pakistan is already affected strongly by flooding. Serious floods caused extensive damage in almost every year between 1985 and 2018 (Figure 11), with between one thousand and 20 million people being affected annually. Further data on the World Bank’s Climate Change Knowledge Platform shows that between 1900 and 2018, a total of 93 flood events were recorded in Pakistan, which is equivalent to 45% of all natural-hazard events in Pakistan in that time interval.

> 22 Full description: “Potential annual daylight work hours lost for work at 300W, %; based on a business as usual scenario (RCP 8.5, average of HADGEM2 and GFDL models) current (1995) and up to 2085”, see UNDP (2016).

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Key Natural Hazard Statistics for 1985-2018 <!-- Start of picture text --> Number of People Affected<br>100M<br>1M<br>a<br>a<br>fo)<br>& 10k | |<br>1985 1990 1995 2000 2005 2010 2015<br>@ Flood<br><!-- End of picture text -->

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Table 6: Current and estimated probabilities for flood damages of a given size

|**Damage**|**1°C of**|**2°C of**|**3.7°C of**|
|---|---|---|---|
||**warming**|**warming**|**warming**|
|**0**|91.233%|82.467%|43.034%|
|**1.04%**|5.000%|10.000%|32.490%|
|**1.69%**|2.000%|4.000%|12.996%|
|**2.17%**|1.000%|2.000%|6.498%|
|**2.78%**|0.400%|0.800%|2.599%|
|**3.02%**|0.200%|0.400%|1.300%|
|**3.49%**|0.100%|0.200%|0.650%|
|**3.65%**|0.067%|0.133%|0.433%|

Source: UNISDR (2015), Myhre et al. (2019) and author’s calculations

## _Model modifications_

Damages are given in terms of the reconstruction cost of the damaged capital. For example, if a flood destroys a bridge and the reconstruction cost of the bridge is 10 million USD, then the official damaged caused by the destruction of the bridge is 10 million USD. However, the economic damage caused by the destruction of that bridge might be far higher than 10 million USD (Hallegatte & Vogt-Schilb, 2016) because the productivity of existing capital is higher than the productivity of marginal capital, which determines the replacement cost of existing projects.<sup>24</sup> To accommodate this reality, the modeling must evaluate the productivity of investment to replace damaged capital goods differently from the productivity of new investment at the margin. In the model, it is assumed that the productivity of damaged capital is equal to the average productivity of capital. This is equivalent to assuming that the productivity distribution of damaged capital is the same as the productivity distribution of the existing capital stock or assuming the projects damaged are randomly selected from the total capital stock.

# 3.8.7 Tracking unrepaired damages

As modeled, capital destroyed by a flood has average capital productivity, while newly constructed capital has marginal productivity. In the modeling we distinguish between two types of capital: damaged capital and normal capital . The economic impact of a unit of damaged capital is equal to the average productivity of capital times the damaged capital . The level of damaged capital at time and is tt tt DDSS KK YY determined according to the following formula tt KK ∗DDSS SS , DD where is new damage in time , is the stock of unrepaired damage and tt tt−1 tt tt is investment into DDSS = DDSS + DD −II repairing damage. Note that this form keeps the memory of the capital stock that would have depreciated DD tt tt tt DD SS DDSS II if was not destroyed. In the modeling the amount of total investment the government can undertake is determined by the fiscal rule. Given that the capital destroyed by floods has an average capital productivity, it would be tt II optimal to direct all investment to reconstruction. However, only a share of total government investment

> 24 This is a natural outcome of diminishing marginal productivity, which implies that average productivity of existing capital is greater than the productivity of new investments.

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is typically allocated to replacing damaged capital, partly because damaged capital tends to be located in a small geographic area and investment priorities in other geographic and sub-national areas continue to be taken out, and because the required machinery (or skills) for reconstruction are not always available. To reflect these practical realities the model assumes that reconstruction investment cannot exceed a share of total investment. φφ }. DD where for the purposes of this paper tt . tt tt II = min{DDSS , φφII To accommodate the idea that the economic impact of damaged capital exceeds that of marginal φφ= 0.5 To accommodate the idea that the economic impact of damaged capital exceeds that of marginal capital, potential output is modified once more to explicitly account for the higher productivity of destroyed capital. Thus ∗ ∗ K −<sup>YY</sup> �(1 −h � YY αα ββ γγ tt VVGGDD tt tt tt tt−1 EE,tt tt Where the final term reflects the lost output from the (time variant) damaged capital, and YY = (1 + DD∗ωω )AA )CC KK YY DDSS reflects the KK sum of undamaged capital and damaged capital. Thus when = 0 the equation reverts to the KK standard potential output equation inclusive of other damages. Note that the treatment of damages is tt DDSS based on aggregate capital. Without distinct forms of capital, the assumption that destroyed capital has an average capital productivity might be underestimated in the case when destroyed key infrastructure renders labor and other capital unutilized. It might over-estimate the productivity effect when capital in the “middle of nowhere” is destroyed.

# 3.9 Adaptation module

PAKMod also includes logic to capture the impacts of adaptation investment to reduce the damages from climate events. Examples include storm proofing roads so they do not get damaged during floods, providing air-conditioning or emergency air-conditioned shelters to reduce the health and productivity impacts of heat waves, strengthening irrigation and drainage systems to limit the impacts of droughts and excess rainfall on agricultural output, and building sea walls to hold back rising oceans.

While evaluating the costs of adaptation investments is easy (assuming they are recorded in a budget tagging exercise), much less is known about the benefits they will generate. Adaptation is mostly analyzed at a project level and hardly ever quantified. Nor are the trade-offs accounted for, what other investments and benefits from investment did not get made in order to pay for an adaptation investment In PAKMod we do not consider private-sector adaptation: adaptation that is undertaken by individuals recognizing that climate change has modified or will change the private return from a given activity. Such private-sector adaptation is undertaken when the private returns from the investment will equal or exceed the costs of undertaking it. In agriculture this might include things like changing crops to more drought resistant varieties<sup>25</sup> or purchasing (or paying more for) crop insurance. In the manufacturing sector, it could mean air-conditioning a shop floor because the loss in productivity of not airconditioning would exceed the cost of providing the air conditioning. These kinds of substitution will

> 25 Indeed, in the damages discussion we netted out such savings from endogenous adaptation behavior when calculating the losses associated with higher temperatures in agriculture.

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occur naturally in line with price signals through the standard mechanisms of the model. The kinds of adaptation investment covered here are ones undertaken by the public sector because the social gains of the investment exceed the social costs, and might include building a dyke around a community which would cost much less than each household providing individual protection, or hardening a roadway so that it did not wash out interrupting commerce between two cities. For additional literature regarding different types of adaptation see Appendix 3A.

# Introducing adaptation into PAKMod

To accurately model the economic consequences of adaptation we need to link spending on adaptation to the amount of damages avoided at a national level. Unfortunately there is little literature outlining the effectiveness of adaptation, indeed, we found only one source relating spending on adaptation to avoided damages (Multi-hazard Mitigation Council 2019).<sup>26</sup> The report refers only to investments in buildings in the United States, however.

Given the lack of direct data, a more speculative approach was taken constructing a protection function that linked spending on adaptation to damage avoided in a flexible fashion, such that parameters can be refined as more information becomes available. The approach here follows that of de Bruin, Dellink and Tol (2009) and De Cian _et al._ (2016). As with Lecocq and Shalizi (2007), de Bruin, Dellink and Tol (2009) Bosello, Carraro and De Cian (2010), and Millner and Dietz (2015), we assume that the product of adaptation investment is adaptation capital<sup>27</sup> that capital does not have a productive use. Adaptation capital works only to reduce damages. It is further assumed that a country that adapts, will implement the projects that offer the most protection per unit of expenditure first, and that subsequent adaptations will generate less protection (decreasing returns). As discussed earlier in PAKMod total public investment is split into adaptation investment ( ) and productive investment ( ) and the stock of adaptation ( ) and productive capital ( ) are tracked II<sup>VV</sup> separately. Adaptation capital protects the economy from climate-related damage to productive capital VV tt tt II<sup>GGPP</sup> KK KK (say flooding). The more adaptation capital an economy accumulates the less productive capital will be damaged by natural disasters. If a road is constructed in a resilient manner (perhaps larger and more frequent culverts to reduce the likelihood of washout), the cost of the road beyond the standard cost is not considered as productive and is treated as an investment in the adaptation capital stock. From a societal point of view adaptation investment makes sense if the productive capital saved (protected) by the investments exceeds the amount of investment forgone in order to make the adaptation investment. The level of adaptation capital accumulates in the same way as productive capital and is assumed to have the same rate of depreciation, VV as productive capital. tt KK . δδ, VV VV VV tt tt−1 tt KK = (1 −δδ)KK + II

> 26 (World Bank 2010) attempts calculate the costs of different adaptation investments, but there is limited information on how much protection (damage forgone) different investments yield. The (Multi-hazard Mitigation Council 2019) estimates the ratio between the cost of adapting buildings to natural disasters and the damages avoided with these investments. The organization finds that 11 dollars can be saved for 1 dollar invested in the case of applying international building codes. However, these data are estimated for the Unites States and apply to natural disasters generally, without explicit links to climate change. The results are thus not easily transferable to climate change adaptation in Pakistan.

> 27 (Bosello, Carraro, and De Cian 2010), by contrast, use a flow variable for adaptation albeit in a model framework where a period is 10 years long.

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We define the amount of adaptation capital that would be obtained if the government invests the equivalent of the annual expected climate damage from the source being protected as . This is considered a maximum because if the government alternatively invested this amount in productive KK<sup>VVssbbII</sup> capital, then it would be able to replace the damaged capital directly at the same cost as doing the adaptation (assuming $1 of investment generated 1$ reduction in capital destruction). If expected damages rise proportionately with GDP, then will rise in step with GDP and therefore , where _g_ is the rate of growth GDP. KK<sup>VVssbbII</sup> VVssbbII VVssbbII KK Inserting this in the equation for the accumulation of adaptation capital, and substituting expected Gross Damages tt = (1 + yy)KK���� for tt−1 gives the tt ����� GGDD II<sup>VV</sup> =<sup>1 + yy</sup> KK<sup>VVssbbII</sup> GGDD. Not all adaptation capital projects will offer the same level of protection per unit of adaptation capital. To yy+ δδ Not all adaptation capital projects will offer the same level of protection per unit of adaptation capital. To account for this, we assume a declining marginal increase in protection in line with the literature (de Bruin, Dellink, and Tol 2009). The amount of protection, , from a given level of adaptation capital can then be expressed as a function of the ratio of actual investment and the maximum adaptation capital PP KK<sup>VV</sup> stock, KK<sup>VV</sup> 2 1<sup>KKVV</sup> γγ , � � PP= γγ where 2 < 1 is a parameter that indicates the extent of diminishing returns to protection. The KK<sup>VVssbbII</sup> higher 2 the less diminishing returns are, such that the limit of 2 = 1 implies that the marginal 0 < γγ protection of adaptation capital is constant. 1 represents the effectiveness of adaptation investments, if γγ γγ 1 equals one and protection would be complete ( ). γγ γγ We follow de Bruin, Dellink and Tol (2009) and De Cian KK<sup>VV</sup> = KK<sup>VVssbbII</sup> _et al._ (2016) in distinguishing between gross ( PP= 1 ) and residual damages (RD).Residual damage from a given climate even in year t, , is equal to the product of gross damage in that year, , and the level of protection at time , GGDD tt RRDD tt . tt GGDD PP SS An economy without adaptation capital ( tt ) will be affected by the full force of climate damage. A tt tt RRDD = (1 −PP )GGDD country that achieves a protection level of one would experience zero damages from flooding. PP= 0 The amount of investments is a policy choice of the government. We can expect the government to set ���� (the expected amount of damages in any given year). Otherwise the economy would expend KK<sup>VV</sup> more resources on adaptation investments than actual expected flooding damage. How much less than ���� KK will depend on the opportunity costs of the money spent on the investments, the effectiveness of<sup>VV</sup> ≤GGDD different levels of protection and society’s tolerance for variability in the level of output. GGDD Because there is very little data that would allow for a convincing calibration of the effectiveness of flooding adaptation in Pakistan, we follow de Bruin, Dellink, and Tol (2009) in setting the curvature parameter of the protection function, 2 , to 0.3 (close to the inverse of their parameter of 3.6). The effectiveness parameter is set to 1 = 1 , implying that if an amount equal to the expected value of γγ flooding damages were invested each year in adaptation, the productive capital stock would be fully γγ 55
protected from flooding. For a description and robustness results for different calibrations, see examples in Burns, Jooste, and Schwerhoff (2021).

### 3.9.1 Accounting for the opportunity costs of adaptation investments

The investment into adaptation capital needs to be considered in the calculation of GDP and government expenses. On the demand side, the standard GDP and government spending identities are modified to account for spending on adaptation capital as follows: When running simulations, the economy-wide effects of adaptation investments will depend strongly on how the government finances adaptation investments. The government can finance these investments through higher deficits, higher taxes, reduced expenditure in any of its expenditure categories, or a combination of these.

# 4. Simulation properties

PAKMod can be used in standard forecasting, policy analysis and fiscal sustainability analyses. Its additional climate features mean that in addition to the normal macroeconomic indicators that are generated (growth, inflation, current account, fiscal account, and unemployment outcomes), the model also will generate climate outcomes, including carbon emissions produced, air pollution, and associated reductions in labor productivity, health, capital destruction and agricultural production. These results are generated whether the focus of the analysis is generating a long-term baseline, analyzing a policy change that has nothing to do with climate, or analyzing the economic impacts of alternative global climate scenarios and different climate policies.

## 4.1 The emissions module

This section presents model properties in response to climate change. The following subsection discusses the baseline and presents the outputs of the model for several standard climate-change scenarios. This includes results from the imposition of a simple carbon-tax under three scenarios. In scenario 1 the government uses the additional revenues to reduce debt (i.e. as a savings device), in scenario 2 the government imposes a revenue neutral carbon policy via a reduction in income taxes and in scenario 3 the government uses the revenue from the carbon tax as a transfer to households.

### 4.1.1 The baseline

To evaluate the model’s climate properties, its outputs are compared with those coming from various Integrated Assessment Models<sup>28</sup> (IAMs). This requires both creating a baseline or “business as usual” (BAU) scenario, which will serve as a reference for various policy scenarios. To anchor the baseline a set

> 28 Integrated Assessment Models (IAMs) are integrated climate-macro models, which are constantly developed by entire teams of researchers over long time horizons (for more see https://www.iamcdocumentation.eu/). They also form an important basis for the assessments of the Intergovernmental Panel on Climate Change (IPCC). We can thus use their baseline scenarios as a benchmark for our baseline. In this subsection we will thus discuss what the benchmark baselines look like and then how to approach the baseline.

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<!-- Start of picture text --> —<br>—<br>—<br><!-- End of picture text --> <!-- Start of picture text --> Figure 1.9 LCOE of onshore wind projects and global weighted average by year of commissioning,<br>1983-2018<br>0.3 e<br>- 2 -e = :<br>-eoon, 2 @2 a<br>< OF i ——- —_— = = .<br>=a e Ps >= -- Oo, Sei = - 2 ue2 ays- = .<br>Z © -etseaerie _@e = . ee -°<br>oe ea. 23: %33:.-<br>so= - & Neteceteesez S é lei & ass:2 é<br>ODPee SagOF S828SS OOBakeOG 2+ERG eeee 8:<br>et TEARS S), 081s) HEE Bae:<br>. og es ta Pas Pie<br>-e°e@--@ @72:6_@ eyss<br>SS —$——Te $< SST<br>1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019<br>Capacity MW ° 100 @ 200 @ 300 @ > 400<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 2.2 Total installed cost for utility-scale solar PV projects and the global weighted average,<br>2010-2018<br>YO. 000<br>9.000 ——g $s SSsSsSsSsSsSSSS<br>8000 : a<br>————= ———__@______,<br>Ct a a<br>~ 6000 Wu) (%) A n .<br>B ¢ i) 4 i & : ‘<br>eo o ¢) _§ @ @<br>oy i ' oe 2 6<br>5 percent (7 2569 i gs fe) Wen HN<br>ww Q )) hol 2323 f, an | ene<br>1000 ; 8 Us Ne) Rey 1309 ig<br>mms Weighted average ey > uy \y wys =<br>9 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Capacity MW “1 @10 = @w 6 >300<br><!-- End of picture text -->
other model projections as in Figure 12.<sup>31</sup> The model’s dynamics are based on the estimated elasticities, rigidities (prices and wages) and speed of adjustment to equilibrium. The CES structure for energy demand differentiates between short and long-run elasticities. This is in line with the discussion earlier where the existence of sunk costs in energy plants means that the economy will not react immediately to the imposition of a carbon tax. Older plants may continue to run as long as their marginal costs (net of sunk construction costs) lie below the net of tax market price of energy. Full adjustment will occur only gradually as these plants age and are eventually scrapped because renovation costs would be too high. Fossil fuel fired plants may be abandoned immediately when the operating costs of existing plants exceed the electricity price. This might apply to plants near the end of their life cycle. An agreement between Pakistan and the Russian Federation (Pakistan Stream Gas Pipeline) is a multibillion-dollar project that aims to connect LNG terminal between the south of Karachi and Lahore in the north-east.<sup>32</sup> While gas emits considerably carbon dioxide, the sunk capital into this project will inevitably slow down the nocarbon energy transmission in the short-term.

# 4.1.2 Mitigation scenarios

The model changes described above allow us to examine the economic impact of different policies to reduce carbon emissions. In what follows a carbon price of 2,500 Pakistani rupees (PKR) per ton of CO2 (roughly $20) is introduced, under three alternative assumptions about the uses made of the revenues collected. In the first, additional revenues are recycled by expanding the social security system, effectively transferring the collected revenues to households to spend on their own priorities. In the second the revenues are used to reduce debt. In the third, the additional revenue is used to reduce other taxes, notably the direct tax rate (the revenue neutral tax reform scenario).

The carbon tax is applied in real terms, thus the nominal amount rises in line with inflation over time. While the amount of the carbon tax is somewhat arbitrary, it is roughly in line with the price in the European Union Emissions Trading System (EU ETS), which has varied between 20 and 30 USD between September 2018 and July 2020. Imposing a $20 carbon tax in the context of Pakistan implies a much larger than $20 per ton price increase because currently, hydrocarbon fuels are subsidized. These subsidies mean that the implicit price of carbon has been negative (and applied at different rates for different products). For example, a $20 carbon tax would imply an increase in the carbon tax on natural gas of more than $60 (currently the carbon content of natural gas is subsidized at just a bit more than $40 per ton).

> 31 It is worth noting, however, that these outcomes are significantly lower than the projections produced by the Government of Pakistan in the context of the 2015 Paris Agreement. Pakistan’s “Intended nationally determined contributions” (INDC) (Government of Pakistan 2016) submission implied a near tripling of C02 emissions, the bulk of which was expected to come from the energy sector. Indeed, data submitted by the authorities implied that the rate of growth of emissions from the energy sector would triple an average annual growth rate of 4.7 percent between 1994 and 2015 to one of 11 percent for the period 2015 to 2030, an acceleration that the authorities attribute to an “accelerated growth scenario of the energy sector” (Government of Pakistan 2016).

> 32 https://www.bloomberg.com/news/articles/2020-12-16/pakistan-to-start-building-lng-pipeline-with-russia-in-july

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# Carbon price by fuel type (2011 USD)

<!-- Start of picture text --> 40<br>0<br>—— Carbon price for coal (2011 USD)<br>-40 —— Carbon price for oil (2011 USD)<br>—— Carbon price for gas (2011 USD)<br>-80<br>-120<br>-160<br>80 90 00 10 20 30 640 50 60 70 = 80 90 00<br><!-- End of picture text --> Carbon price by fuel type (2011 PKR) <!-- Start of picture text --> 4,000<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>—— Carbon price for coal (2011 PKR)<br>-4,000 —— Carbon price for oil (2011 PKR)<br>—— Carbon price for gas (2011 PKR)<br>-8,000<br>-12,000<br>-16,000<br>-20,000<br>80 90 00 10 20 30 §640 50 60 70 = 80 90 00<br><!-- End of picture text -->
Total emissions/Total emissions in 2017 <!-- Start of picture text --> 3.0 —— Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Carbon tax and reduction in direct taxes<br>— Scenario 3: Carbon tax and an increase in hh. transfers<br>25.<br>—— Baseline<br>2.0<br>5<br>1.0<br>0.5<br>0.0<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br><!-- End of picture text -->

# Renewables/Energy

<!-- Start of picture text --> 90 —— Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Carbon tax and reduction in direct taxes<br>80 — Scenario 3: Carbon tax and an increase in nsfers<br>—— Baseline<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br><!-- End of picture text -->
Figure 19 reports the simulated impact of the imposition of the carbon price on the macroeconomy, under each of the three revenue disposal scenarios. In scenario 1, Higher prices and inflation cause GDP and consumption to fall initially compared with the baseline. Energy prices increase, so that production becomes more expensive, although the impacts are modest -- less than 1% loss in GDP and less than 2% in consumption. Subsequently, the economy catches up with the baseline for GDP, as the economy adjusts to the new price levels and because the revenue from the carbon tax allows the debt to be paid down. This in turn lowers debt service charges and frees up economy-wide savings for private sector investment. Lower debt levels and reduced demand for savings, reduces the sovereign risk premium and the cost of investment and encourages more private investment, and results in a higher capital stock and eventually higher levels of potential GDP.

In scenario 2, carbon tax revenues are used to reduce taxes on capital and labor in the formal sector. The reduction in these taxes alleviates the initial income losses due to higher energy costs. As a result, the initial GDP and consumption impact is positive. Note that the initial response in consumption reacts to an increase in disposable income immediately. Liquidity constrained households spend the higher incomes immediately which causes the initial growth in this scenario to exceed the baseline. While the level effect is larger than the baseline in subsequent periods, the growth in consumption is more moderate in those periods. The new relatively broad carbon tax causes fewer economic distortions than the direct taxes that it replaced. In particular, the decrease in the labor tax (which falls only on formal employment) increases formal employment where productivity is higher and as a result overall output is boosted. Lower direct taxes also reduce the cost of investment, which contributes to an uptick in the capital stock and potential GDP.

In scenario 3, carbon tax revenues are recycled back into the economy as a transfer to households. As a result, despite higher consumer prices, increases in household income translate into an initial boost in GDP and consumption that is only gradually eroded by inflation. In the long run the income effect is eroded by higher prices and so GDP gains are minimal. Higher prices generate a small reduction in debt to GDP, but this is mainly due to the inflationary response.

In all three scenarios increased hydrocarbon prices reduce imports (lower right panel of Figure 19), which as an initial effect, reduces the current account deficit and the domestic demand for foreign saving, contributing to a reduction in domestic interest rates and capital accumulation.

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<!-- Start of picture text --> GDP, percent deviation from baseline Consumption, percent deviation from baseline<br>5 —— Scenario 1: Carbon tax and debt repayment 6 —— Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Ca tax and reduction in direct taxes —— Scenario 2: Carbon tax and reduction in direct taxes<br>—— Scenario 3: Carbor\fex and an increase in hh. transfers 5 —— Scenario 3: Caron tex and an increase in hh. transfers<br>4<br>4<br>33<br>22<br>ak<br>1<br>i)<br>0 -1<br>-1 -2<br>10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br>Debt/GDP, difference to the baseline in percentage points Imports/GDP, difference to the baseline in percentage points<br>0 —— Scenario 1: Carbon tax and debt repayment 0.2 —— Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Carbon tax and reduction in direct taxes —— Scenario 2: Carbon tax and reduction in direct taxes<br>—— Scenario 3: Carbon tax and an increase in hh. transfers 0.0 —— Scenario 3: Carbon tex and-an increase in hh- transfers<br>2<br>0.2<br>4 0.4<br>-6 0.6<br>0.8<br>8<br>-1.0<br>ap -1.2<br>-12 -14<br>10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br><!-- End of picture text -->
<!-- Start of picture text --> Government revenue/GDP, difference to the baseline in percentage points Government deficit/GDP, difference to the baseline in percentage points<br>28 —— Scenario 1: Carbon tax and debt repayment 3.0 —— Scenario 1: Carbon tax and debt repayment<br>24 —— Scenario 2: Carbon tax and reduction in direct taxes —— Scenario 2: Carkon tax and reduction in direct taxes<br>;<br>20 —— Scenario3: Ca tax and an increase in hh. transfers 25 —— Scenario3: Caren tax and an increase in hh. transfers<br>1612 s.\ : 2015 7<br>08<br>04 a 10 i<br>0.0 _-ae IN,I, ———<br>0.408 ieSS— 0.50.0 /i ws:a———nt =<br>10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br>Carbon tax revenue/total revenue, in percent Direct tax revenue/total revenue, in percent<br>20 —— Scenario 1: Carbon tax and debt repayment 30 —— Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Carbon tax and reduction in direct taxes —— Scenario 2: Carbon tax and reduction in direct taxes<br>10 Scenario3: Carbon tax and an increase in hh. transfers Scenario3: Carbon tax and an increase in hh. transfers<br>——s i 25 \ | Baseline<br>-102 20 || \<br>15 | | SS = Le<br>-20 |] : Pf<br>-30 10 __e| ~ ,<br>-50 Oo<br>10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br><!-- End of picture text -->
Unemployment rate, difference to the baseline in percentage points <!-- Start of picture text --> 1<br>0<br>-1<br>-2<br>-3<br>—— Scenario 1: Carbon tax and debt repayment<br>7Py ScenarioScenario 2:3: CarbonCarbon taxtax andand anreductionincreasein indirecthh. transfers taxes<br>5<br>10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br><!-- End of picture text -->

# Informal employment/total employment, difference to the baseline in percentage points

0.5 <!-- Start of picture text --> 0.0<br>Scenario 1: Carbon tax and debt repayment<br>-0.5 Scenario 2: Carbon tax and reduction in direct taxes<br>Scenario 3: Carbon tax and an increase in hh. Ssters<br>a<br>-1.5<br>-2.0<br>-2.5<br>-3.0<br>10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br><!-- End of picture text -->
# Working days lost/labor force, in percent

<!-- Start of picture text --> .26<br>—— Scenario 1: Carbon tax and debt repayment<br>.24 —— Scenario 2: Carbon tax and reduction in direct taxes<br>22 — Scenario 3: Carbon tax and an increase in hh. transfers<br>—— Baseline<br>.20<br>18<br>16<br>14<br>42<br>-10<br>2010 2015 2020 2025 2030 2035 2040<br>Deaths attributable to air pollution<br>35,000<br>— Scenario 1: Carbon tax and debt repayment<br>30,000 —— Scenario 2: Carbon tax and reduction in direct taxes<br>—— Scenario 3: Carbon tax and an increase in hh. transfer,<br>25,000 :<br>—— Baseline<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>2010 2015 2020 2025 2030 2035 2040<br><!-- End of picture text --> Health expenditure attributed to pollution/Government consumption, in percent <!-- Start of picture text -->
-06<br>-05<br>-04<br>-03<br>———. Scenario 1: Carbon tax and debt repayment<br>——. Scenario 2: Carbon tax and reduction in direct taxes<br>-02<br>— Scenario 3: Carbon tax and an increase in hh. transfers<br>—— Baseline<br>-01<br>2010 2015 2020 2025 2030 2035 2040<br><!-- End of picture text -->
# _More ambitious climate policy_

In the next set of simulations, the implemented carbon price is increased to incite an even larger decarbonization of the economy. Initially the carbon price is the same as in the previous simulations but starting in 2021 the carbon price is increased a further 5% per year in real terms. This results in real carbon prices of 30 USD (27 EUR) and 60 USD (54 EUR) in 2027 and 2041, values at the low-end and midpoint the OECD (2018) estimates of the carbon prices among OECD member countries in 2020. Sweden has a carbon tax of 119 USD already (World Bank 2020a), a level not reached in this scenario until 2057. Because of compounding the carbon price in the end period reaches a very high of about 1400 (2011 USD) in 2100. As Figure 23 shows, this level of climate policy slows the rise in energy-related emissions, but they nevertheless almost double from 2017 levels.

In the steady state, after full adjustment to the very high carbon prices at the end period, carbon levels are about 70 percent higher than the 2017 level, whereas it would have been roughly 150 percent higher with no carbon price. In all three revenue-use variants carbon emissions from energy per unit of GDP decline to close to zero (Figure 23).

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<!-- Start of picture text --> Carbon price by fuel type (2011 USD) Carbon price by fuel type (2011 PKR)<br>1,600 140,000<br>1,200’ —— Carbon price for coal (2011 USD) 120,000100,000 —————— Carbon CarbonCarbon priceprice forprice forfor oi! gas coal(2011 PKR)(2011(2011 PKR)PKR)<br>—— Carbon price for oi! (2011 USD)<br>800 | ——Carbon price for gas (2011 USD) 80,000<br>60,000<br>400 40,000<br>20,000<br>0<br>-400 Poe<br>80 90 00 10 20 30 40 50 60 70 80 390 00 -20,00080 90 00 10. 20 30 40 50 60 70 80 90 00<br>Total emissions/Total emissions in 2017 Emissions/GDP<br>3.0 10<br>—— Scenario 1: Carbon tax and debt repayment<br>2.5i ———— ScenarioScenario 2:3: Carbon Carbon taxtex andand reductionan increasein indirecthh. trataxes 8<br>—— Baseline<br>2.0<br>6<br>15<br>1.0 r —— Scenario 1: Carbon tax and debt it<br>. —— Scenario 2: Carbon tax and reduction in dirt<br>0.5 2 —— Scenario 3: Carbon tax and an increase in hh. transfers<br>—— Baseline<br>0.0 0<br>80 90 00 10 20 30 40 50 60 70 80 90 00 80 90 00 10 20 30 40 50 60 70 80 90 00<br><!-- End of picture text -->
<!-- Start of picture text --> Carbon price by fuel type (2011 USD)<br><!-- End of picture text --> <!-- Start of picture text --> 14,000<br>12,000<br>10,000 —— Carbon price for coal (2011 USD)<br>—— Carbon price for oi! (2011 USD)<br>8,000 —— Carbon price for gas (2011 USD)<br>6,000<br>4,000<br>2,000<br>0 =<br>-2,000<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br>Total emissions/Total emissions in 2017<br>3.0 — Scenario 1: Carbon tax and debt repayment<br>—— Scenario 2: Carbon tax and reduction in direct taxes<br>2.5 —— Scenario 3: Carbon tax and an increase in hh. transfers<br>—— Baseline<br>2.0<br>15<br>1.0<br>0.5E —<br>0.0<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br><!-- End of picture text --> <!-- Start of picture text --> Carbon price by fuel type (2011 PKR)<br><!-- End of picture text --> <!-- Start of picture text --> 1,200,000<br>1,000,000 —— Carbon price for coal (2011 PKR)<br>—— Carbon price for oi! (2011 PKR)<br>800,000 — Carbon price for gas (2011 PKR)<br>600,000<br>400,000<br>200,000<br>0 =<br>-200,000<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br>Emissions/GDP<br>10<br>8<br>6<br>4<br>—— Scenario 1: Carbon tax and it repayment<br>—— Scenario 2: Carbon tax and redu jn direct taxes<br>2 = —— Scenario 3: Carbontax and an increasei insfers<br>—— Baseline<br>0<br>80 90 00 10 20 30 40 50 60 70 80 90 00<br><!-- End of picture text -->
Figure 25: GDP in the case of highly ambitious climate policy 8% per year

## **GDP, percent deviation from baseline**

<!-- Start of picture text --> 5<br>Scenario 1: Carbon tax and debt repayment<br>4<br>Scenario 2: Carbon tax and reduction in direct taxes<br>Scenario 3: Carbon tax and an increase in hh. transfers<br>3<br>2<br>1<br>0<br>-1<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->

# 4.2 Climate change and economic performance

As discussed above, climate change will have deleterious effects on the economy. This section quantifies the impacts on labor productivity, agricultural output from higher temperatures and increased rainfall variability, including the damage caused by flooding.

Three simulations are presented. The first is a counterfactual baseline scenario where it is assumed that there will be no further climate change. This hypothetical baseline case is only presented to help quantify the economic consequences of the climate change that Pakistan is likely to undergo over the coming decades. Two additional simulations are based on two climate scenarios developed by climate scientists, see Table 7. These damage scenarios are based on the representative concentration pathways RCP 2.6 and RCP 8.5. RCP 2.6 is used to reflect an attainable mitigation scenario, being the pathway closest in line with the targets of the Paris Agreement. RCP 8.5 is used as a “Business as Usual” (BAU) scenario. While we recognize that there is an emerging consensus that without climate policy, emissions will not be as high as assumed in the RCP 8.5 scenario (Hausfather and Peters 2020), we nevertheless use RCP 8.5 because of the abundance of data and analysis that exists for this scenario, which should facilitate evaluation of PAKMod results.

PAKMod includes five kinds of climate damages, two damages associated with changed productivity due to climate change (declines in labor productivity due to higher temperatures; and declines in agricultural productivity due to higher temperatures and changes in rainfall patterns); two damages associated with reduced labor supply (days lost and lives lost due to pollution); and one associated with the increased incidence of extreme weather events (increased incidence of flooding).

In order to link the RCP scenarios to temperature changes, we use the temperature estimates for the RCPs provided in IPCC (2014b), see Table 7. The year 2020 is the first year of the forecast period. The data for 2050 and 2100 are taken directly from Table SPM.2 in IPCC (2014b). The value of 0.5 for the year 2020 is based on a simple linear interpolation of the IPCC (2014b) forecast that ”the global mean surface 72
temperature change for the period 2016–2035 relative to 1986–2005 will likely be in the range of 0.3°C to 0.7°.

Table 7: Projected change in global mean surface air temperature (mean estimate) for the mid- and late 21st century relative to the reference period of 1986–2005

||**2020**|**2050**|**2100**|
|---|---|---|---|
|**Baseline**|0.5|0.5|0.5|
|**RCP 2.6**|0.5|1.0|1.0|
|**RCP 8.5**|0.5|2.0|3.7|

Source: IPCC (2014b)

# 4.2.1 The impact of heat on labor productivity

The effects for the two RCP scenarios are shown in Figure 26. RCP 2.6 is shown in blue and RCP 8.5 in orange. Each are expressed as a percent deviation from the counterfactual baseline where there is no climate change. In each scenario, higher temperatures reduce labor productivity and therefore potential output and production. This has direct impacts on GDP, consumption and private investment in the top panels. Overall, in the RCP 8.5 scenario, PAKMOD suggests that lower labor productivity will shave about 6 percent from GDP and consumption.

In line with decreasing economic activity government revenue decreases. Government expenditure follows government revenue closely as in these long-term simulations the fiscal rule is designed to ensure fiscal sustainability. Government debt increases slightly (and later decreases) because government expenditure follows the changes in revenue with a lag.

In the model, reductions in labor productivity result in a decline in potential GDP relative to market price GDP in the immediate short run. This results in moderate (but negligible) inflation. The rise in prices and the reduction in labor productivity decreases both the consumer and producer real wage through the marginal productivity of labor channel.

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<!-- Start of picture text --> Response in key variables to shocks<br>GDP, in percent compared to baseline ‘Consumption, in percent compared to baseline Prt. Investment, inpercent compared to baseline<br>111<br>ee2abe 8 a =: Ss ee sat2a a<br>2 ‘ ‘ 2<br>—— Scenario: REP: 2.6 3 b \<br>3 ~ —— Scenario: AEP: 8.5 4 3 .<br>e ae . ais 4 ——<br>= _———_ 6 Se > ~—<br>6 7 6 =<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Government revenue, in percent compared to baseline Government expenditure, in percent compared to baseline Government debt, percentage point deviation from baseline<br>1 1 16<br>o-~ o- 12 =<br>= a | ox} \<br>\ ] \<br>2 2 \ os | 4<br>3 ‘<! 3 oo +<br>4 1 a ee 4 (== —_ —— 04 . ee<br>3 5 08<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 «2030 «462040 «462050 462060 42070 42080 2090 2100 2020 2030 42040 42050 2060 2070 2080 2090 2100<br>Consumer prices difference ingrowthrates Real consumer wage, differencein growth rates Real producer wage, difference ingrowth rates<br>a7 OS O5<br>26os / ~ \ 20| a ee- ee= ——— S| CS a a tfSe<br>/ \ -25 a -05 | ——<br>23 / =10 at -10 ge<br>02 \ ‘ — -AS - -AS Pal<br>a ||| /[(o~ ae~ -20 id -20 | Y val<br>“02 -30 -30<br>2020 ©2030 «2040 «2050 «2060 ©42070 +2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 © 2100 2020 «2030 «2040 «2050 «2060 «2070 «2080 2090 2100<br><!-- End of picture text -->
<!-- Start of picture text --> Response in key variables to shocks<br>GDP, in percent compared to baseline ‘Consumption, in percent compared tobaseline Prvt. Investment,in percent compared to baseline<br>4 1 o><br>o~ o~ as<br>“4 a '<br>2 ~ E ay ~<br>— scinago: ACP: 2.6 x ‘ :=<br>¥ Ss ed g ~ <]<br>4 = 4 —— “ a<br>TM2020 2030 (2040 «42050 «42060 42070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 42040 2050 2060 2070 2080 2090 2100<br>Governmentrevenue,in percent comparedto baseline Government expenditure, in percent compared to baseline Government debt, percentage point deviation from baseline<br>oo oo 10 ~_<br>-19 \ \ os \<br>\ 18 \ / \<br>45 \ AS \\ os // \ \<br>‘ \ 02 \<br>2025 \ -20 ‘ a) he \<br>= 25 _ a oe<br>35310 a 30“35. — a 0A06 ae: a ats<br>2020 2030 2040 ©2050 2060 2070 2080 2090 2100 2020 «2030 «2040 «2050 «2060 «62070 «2080 ©2090 «2100 2020 ©2030 «2040 «2050 «2060 «2070/2080 ©=«2090 «2100<br>Consumer prices, difference in growth rates Realconsumerwage, difference ingrowth rates Real producer wage, difference in growthrates<br>OS nk 04 «<br>04 / \ so —— 20 $$<br>23 / \ -04 ee SSSS<br>/} \ j -08 — — “05 ae _——<br>02 i “42 oat -10 So<br>on —~ aes a -16 || -1s |= :<br>00 }# = mag -24oe -20<br>-1 i // Fa “252<br>-02 -32 -30<br>2020 2030 2040 «492050 «2060 2070 42080 2090 2100 2020 2030 42040 2050 2060 2070 2080 2090 2100 2020 ©2030 «2040 «2050 «2060 «62070 «2080 «2090 «2100<br><!-- End of picture text -->
# Fans charts of probabilistic flood shocks. Scenario: RCP 2.6

<!-- Start of picture text --> GDP compared to baseline, in percent Capital stock compared to baseline, in percent<br>LOny {OE onneeannennnnnneeennnnennesnnennensnntentensnntenenennsnnuetuneutestunsntenunennanenmeanteg<br>001 -.005<br>002 | -.010 |<br>-.003 -01s|<br>004 -.020 |<br>“005 -.025 |<br>-.006 -.030 |<br>007 | -035<br>ARHSRRRRARRRRRRRRRRRAR RRRRRRRRRRRRARER RAKES SR<br>Consumption compared to baseline, in percent Debt/GDP, compared to baseline, in percentage points<br>002 008<br>000 - |<br>-.002 .005 |<br>004 004 |<br>| 003|<br>| .001 |<br>| “000<br>7010 MRAP AMh ana 2295 gs “pg 7001 OQPRAKRAEEGREB SeERe<br>RARRERARARRRRRRRSRRSER RAR RRRASRRRRR SAREE<br><!-- End of picture text -->
<!-- Start of picture text --> Fans charts of probabilistic flood shocks. Scenario: RCP 8.5<br>GDP compared to baseline, in percent Capital stock compared to baseline, in percent<br>£00 102<br>00 | |<br>004 | -.02 |<br>-.012 | | -.06 |<br>016 | -.08<br>020 -.10<br>oeRARARRRRRARARRARRRRRARRARRARRESEHAERERERE R a SHR “BERSHMRESERERRERERERRRRARRARARRARRRRRRRRRRRASHR<br>Consumption compared to baseline, in percent Debt/GDP, compared to baseline, in percentage points<br>Fn Be<br>-015 | .002<br>020 .000 |<br>oaRARRARRRARARRRRRRRRRARRARRAMER E GSHEREREREGREER MERERRRARARRARRARRRRRRRRRRRAROESSERQEREROERGER<br><!-- End of picture text -->
<!-- Start of picture text --> Response in key variables to shocks<br>GDP, in percent compared to baseline Consumption , in percent compared to baseline Prvt. Investment , in percent compared to baseline<br>00s 005 00s<br>100 —~ 200 = 000<br>~005 Ne 205 x -005 »<br>010 ‘\ “i -010 x \ ~010 *. :<br>015 — Sconaria: \REZ. -015 ~ ~ -p1s s ~.<br>— Seanaria: RP: 8.35. -020 Th ‘\.<br>~020 hs <p — ~020 bs<br>~025 —— a0 2 -025 ae<br>-030 “035 -030 eo<br>2020 2030 ©2040 «©2050 «2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Government revenue, in percent compar ed to baseline Government expenditure, in percent comparedto baseline Government debt, percentagepoint deviation from baseline<br>004 005 08<br>000<br>mee a » -005200 TS LL 006oa Fs TM x<br>-012 q‘-010 \ , 002 Z} \\<br>016 \~ “015 se . 900 \<br>-020 ie sa -020 aa 002 Ne ae —Taie<br>~024 ~025 -004<br>2020 ©2030 «2040 «62050 «2060 «2070 ©2080 2090 2100 2020 2030 «2040 «62050 «2060 «2070 +2080 2090 ©2100 2020 2030 42040 ©2050 ©2060 2070 2080 2090 2100<br>Consumer prices, difference in growth rates Real consumer wage, difference in growth rates Real producer wage, difference in growth rates<br>00025 a 0001 0001<br>iat / a 2000 =. 2000 =><br>00015 / \ ~0001 Pe ~9001 we<br>0010 / at = -0002 we -0002 in<br>‘ooes /} mo ~ ~0-2 00 34 \\ ~~A - 0o 00 43 \\. 4) p<br>ioe ~0005 \ / 0005 * 7<br>-90005 —_ j oad \ 7 -0006 ‘ 3<br>~90010 2020 2030~~ S2040 ©2050 2060 2070 2080 2090 2100 ~0007~n0082020 2030\ =2040 20504 2060 2070 2080 2090 2100 ~0007~20082020 2030seBe2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->
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# 4.3 Adaptation to climate change

Given the non-linear model structure, the economywide response to adaptation investments depends importantly on the extent of the investments. Similarly, the long run impacts will depend on how they are paid for. To the extent that productive investment is forgone to pay for adaptation investment, the productive capital stock and therefore supply potential of the economy will suffer. These effects are important drivers of the results reported in the following simulations.

## 4.3.1 Impacts of adaptation investment to reduce the impact of flooding

The simulations reported in Figure 32 are somewhat artificial, they show the impact of a very large flood occurring at a regular once in 10 years frequency. The actual distribution of flooding, see Table 6, involves many smaller floods distributed more evenly across time. The illustrative shock is set equal to ten times the average annual flooding damage (for the RCP 8.5 scenario), occurring once every 10 years. The same set of floods were run under different adaptation investment regimes ranging from spending levels equal to 20% of the annual expected damage to 100%.

Figure 32 reports results for some key economic variables for each of these scenarios, with adaptation investment running from 0% of the expected value of damages (the BAU scenario) to 100% of the expected value of damages. Outcomes are presented for GDP, potential GDP, household consumption and the public debt to GDP ratio. In each case, outputs are expressed as a percent deviation from a baseline of a hypothetical version of the Pakistani economy that does not have flooding damages. The exception is the debt-to-GDP ratio, which is presented as the change in the debt-to-GDP ratio.

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# Comparison across different amounts of disaster management investment (RCP 8.5 scenario)

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At low rates of adaptation investment, the benefits of adaptation investment outweigh the opportunity costs coming from reduced government productive investment and other spending. However, as spending levels rise, the average benefit from adaptation declines due to the concave protection function, which means that the return on investment in adaptation capital declines at higher levels of investment. Thus at 20% of expected losses adaptation reduces GDP losses, while also reducing volatility. At 60% the GDP effect mainly disappears as the additional investment in adaptation capital are much less protective than the first and the crowding out of productive investment becomes a dominant effect on GDP. For investment rates higher than 60% of expected losses the crowding effect dominates, and GDP effects are worse than in the no investment scenario. Whether these results are welfare reducing will depend on the importance consumers attach to the smoother profile of GDP in the high adaptation investment scenarios.

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# 5. Unmodeled aspects of climate economics

In addition to the modeling described in the previous sections, an important result of this project is the identification of aspects of climate change, which are important for an aggregate assessment of climate policy, but which could not be included. The unmodeled aspects refer to the macroeconomic modeling (Section 5.1), the impact of climate change (Sections 5.2 to 5.5), adaptation (Section 5.6) and mitigation (Section 5.7). The list is not exhaustive, of course. The effect of climate change on ecosystem services could also be included, for example.

## 5.1 Structural change

While other variants of the MFMod model consider time horizons of only a few years, this model considers a time horizon until 2100. This is necessary to see, for example, the impact of a given climate policy on long-term carbon emissions. In this long time horizon, however, it can be expected that the structure of Pakistan’s economy changes. The share of the service sector, for example increases with development. Countries also shift from informal to formal employment as they develop. For increased accuracy, therefore, this structural change occurring in the baseline could be modeled more directly. This would require factor inputs at a sectoral level and a decomposition of productivity into within and between effects. Structural change would materialize as productive factors are allocated to more productive sectors. This would imply a shrinking of the agricultural sector and an increase in services. In PAKMod structural change arises due to shifts in demand for agricultural and industrial goods as well as demand for services.

Finally, it is not theoretically satisfying to model total labor as a CES aggregate of informal and formal labor. The CES function assumes that there is a complementarity between formal and informal labor. While this is a meaningful assumption for different types of labor, like skilled and unskilled labor, it is not meaningful for types of labor, which differ only by their legal status. A better alternative is the model described by (Ulyssea 2018), a simplified version of which is suggested in the appendix of the section.

## 5.2 Loss of human capital

There is evidence that both natural disasters and high temperature have negative effects on human capital. We present three channels for this link below, but there may well be additional channels.

### 5.2.1 Disaster effect on the human capital of adults

(Yagan 2019) shows that the 2007-2009 unemployment shock of the financial crisis reduced the employment rate in the US, while the unemployment rate returned to the pre-crisis level. This means that a considerable number of workers left the labor force. Testing several possible mechanisms for this effect, the author concludes that the exit from the labor market is caused by two effects: loss of human capital and lower labor demand. Three possible explanations for the reduction in labor demand are given, including productivity shocks. (Blanchard and Summers 1986) and (Reifschneider, Wascher, and Wilcox 2015) also point to a loss in human capital as the reason for permanent effects of an economic crisis.

### 5.2.2 Disaster effect on the human capital of children

Deuchert and Felfe (2015) show that disasters, super typhoons in this case, reduce children’s education, but not their health. According to the authors this effect results from parents shifting their investments away from children’s education. Rosales-Rueda (2018) shows that children who were affected by a natural disaster during pregnancy have lower cognitive outcomes because of decreased nutrition quality available 83
to their mothers. Karbownik and Wray (2019) find that a disruption of health service after hurricanes in the US causes children to have a 5% lower income later in life.

## 5.2.3 Temperature effect on the human capital of children

Isen, Rossin-Slater and Walker (2017) and Fishman, Carrillo and Russ (2019) show that adults who had been exposed to higher temperature in utero have lower income. Isen, Rossin-Slater and Walker (2017) further show that access to air conditioning eliminates the effect almost completely, thus suggesting that the effect is caused directly by the temperature. Lloyd _et al._ (2019) describe a substantial increase of stunting among children due to climate change.

# 5.3 Effect on TFP

There is a large literature (Anzoategui et al. 2019; Benigno and Fornaro 2017; Huber 2018; Duval, Hong, and Timmer 2019; Queralto 2019; Bianchi, Kung, and Morales 2019) that suggests that a crisis reduces aggregate demand and that firms respond to this decrease in part by lowering their investments in research and development. When firms invest less in R&D, productivity growth slows for a period, and this reduces the level of GDP. While these papers consider financial or banking crises, it is plausible that large natural disasters cause a disruption of the financial sector and thus trigger the described reductions in R&D investments and therefore reduce the level of TFP.

# 5.4 Effect on investment

When disasters occur frequently, investors anticipate this and reduce the expected rate of return on investment. As a result, they invest less (Hsiang and Jina 2015). To the extent that the incidence of disasters is unchanged, this effect is included in PAKMod, because it is reflected in historical investment rates. However, if the incidence of disaster were to increase, the additional losses in the productivity of investment is not captured in the model. Introducing a forward-looking aspect into the investment decision, that explicitly incorporates the expected capital losses, would capture such an effect. When disasters occur very frequently, it becomes impossible for countries to maintain economic growth and a disaster-induced poverty trap results (Stéphane Hallegatte and Dumas 2009).

# 5.5 Uncertainty

The damage of climate change maybe grossly underestimated in this model. (Stern 2013) points out that catastrophic outcomes are typically not considered adequately in economic models. This is related to the argument of (Weitzman 2009) that there is a non-negligible risk of apocalyptic catastrophes (which would justify efforts to avoid climate change at all cost). The potential for these dramatic catastrophes is linked to the possibility that tipping points in the earth system triggering positive feedback loops of global heating (Lenton et al. 2008).

# 5.6 Adaptation

In modeling the effectiveness of adaptation, a key equation is the relation between adaptation capital and 2 protection, 1 ~~�~~ A ~~�~~ γγ . While the functional form seems natural and has been used in the KK<sup>AA</sup> literature, the calibration of the parameters is very uncertain. Finding data on these parameters or PP= γγ KK<sup>A</sup> estimating them would strengthen the modeling of adaptation strongly. This would have to be done for each of the modeled damages separately. A starting point could be chapter 17 of IPCC (2014a), which summarizes the state of knowledge on the “Economics of Adaptation”. A subchapter on “Costing 84
Adaptation” gives some indications on methodology but does not provide data that would be directly useful for calibrating the parameters of the protection function.

Further damages of relevance for Pakistan are extreme heat events, landslides, and droughts. For the first two of these, there seems to be hardly any data, although droughts are subsumed in the modeled loss of agricultural productivity.

## 5.7 Distributional aspects

The aggregate nature of PAKMod as a macroeconomic model, precludes consideration of the distributional aspects of climate policy and climate impacts. Climate policy is expected to have a regressive effect in developed countries and a progressive effect in most developing countries (Dorband et al. 2019; Metcalf 2019). Further, the revenue generated in carbon pricing can be used to address distributional effects directly (Klenert et al. 2018; Jakob et al. 2016). These are important aspects for the effect of climate policy on welfare, that could be addressed by pairing PAKMod with a microsimulation model such as the World Bank’s Global Income Distribution Dynamics Model.<sup>34</sup> In addition, natural disasters have a strong distributional effect and climate change is expected to increase the frequency of natural disasters. Low-income households have lower resilience to disaster shocks and are thus affected more strongly. The effect of climate change on poverty has been analyzed in Hallegatte _et al._ (2016) and Hallegatte and Rozenberg (2017). For both of these publications, a microsimulation model was used, for details see the “Supplementary Information” for Hallegatte and Rozenberg (2017). The data of the PAKMod model could be used as an input to the microsimulation model to analyze poverty effects.

Another way to estimate the distributional or welfare losses of climate-related natural disasters is the literature on the welfare implications of business cycles (Cho, Cooley, and Kim 2015; McKay and Reis 2016). The insights of this literature could be transferred to the business cycles caused by disasters.

# 6. Conclusion

This paper documents the practical aspects of integrating climate considerations into a macrostructural model of the type typically used in Ministries of Finance and Economy for forecasting, fiscal sustainability and policy analysis. Such a model can contribute importantly to mainstreaming climate considerations into the regular decision-making process of central economic ministries. Climate outputs, emissions, economic damages, and health impacts of pollution are standard outputs of the model and are presented on an equal footing against other policy priorities, such as unemployment, inflation, and fiscal and monetary sustainability. The model allows climate policy to be examined in an economy-wide context. Just as importantly, it allows the otherwise unanticipated climate effects of policies not directly aimed at climate to be considered.

Five separate climate modules are incorporated into to the World Bank model covering: 1) A more disaggregated energy module; 2) an energy emissions and pollution module; 3) damage functions that reflect the impact of a changing climate on the economy; 4) adaptation and capital protection functions

> 34 See for example <u>https://web.worldbank.org/archive/website01589/WEB/IMAGES/PARAL-31.PDF</u>

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that reflect how investments can reduce the damages that might otherwise occur; and finally 5) informality.

The energy emissions and pollution modules help map out how economic activity affects the environment and climate, and can, in conjunction with different policy levers (including carbon taxes), be used to model various decarbonization strategies. Presented simulations are consistent with those from other climate models. Importantly, economic outcomes depend on what is done with tax revenues. Given the high degree of informality in the Pakistani economy, an approach that reduces taxes that are imposed on a narrower tax base (such as formal labor) could reverse the small negative impact of a carbon tax on GDP.

The modeling also served to highlight the economic consequences of climate change. Simulations based on a significant global warming (RCP 8.5) suggest that reduced labor and agricultural productivity, increased frequency of extreme weather events and higher pollution could combine to reduce GDP by more than 10 percent, as compared with a scenario where the climate did not continue warming or by 1.7 percent as compared to a scenario where additional climate change was limited (RCP 2.6).

The modeling of adaptation is more speculative, but suggests that in the face of a concave protection function the largest benefit from adaptation expenditures would be attained by a level of spending in the range of 25 to 30 percent of expected damages.

While many climate outcomes and considerations are included in this paper, some were excluded principally because of data limitations. These represent areas for further research, calling for empirical work to better understand the economic damages caused by climate change in various sectors through the factors of production and shifts in demand, but also to better quantify the economic benefits in terms of forgone damages accrued by adaptation investments.

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