DISTRIBUTIONAL IMPACTS OF ENERGY POLICIES IN INDIA: IMPLICATIONS FOR EQUITY IN INTERNATIONAL CLIMATE CHANGE AGREEMENTS

A DISSERTATION SUBMITTED TO THE EMMETT INTERDISICPLINARY PROGRAM IN ENVIRONMENT AND RESOURCES AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

NARASIMHA DESIRAZU RAO

AUGUST 2011

© 2011 by Narasimha Desirazu Rao. All Rights Reserved. Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/py027yn9445

ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Lawrence Goulder, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Debra Satz, Co-Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Joshua Cohen

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

David Victor

Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives.

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ABSTRACT

Two-thirds to three-fourths of future global greenhouse gas emissions by 2030 are likely to come from large developing economies. Their participation in mitigating climate change is imperative to achieving the target of restricting global average temperature increase to 2 degrees Celsius above preindustrial levels that was set in the Copenhagen Accord of December 2009. But a third of the world’s poor live in one of these economies, India. While there is much agreement among scholars that climate mitigation should not interfere with the humans’ ability to enjoy a minimal standard of living, there is little scholarship on how to carve out such an “exemption” for poor subpopulations within states in international climate change mitigation agreements. Further, there is little analysis in developing countries of how the impacts of specific mitigation policies would be distributed across the population. This dissertation begins to fill these gaps.

I analyze one class of issues related to the role of state governments that would receive such an exemption from mitigation for their poor, but who may not be accountable in international agreements for how such an exemption is implemented. States influence the poor’s emissions through policy choices and the institutions that implement policies. These policies and institutions affect the number of people that are entitled to an exemption, and whether they would actually receive its associated benefits.

This work consists of three studies, the first two of which are positive studies of the income distributional impacts of potential climate mitigation policies in Maharashtra, India. The first study examines the impact on income distribution of removing the kerosene subsidy. The second study evaluates the leverage electricity regulators have over the distribution of mitigation burdens in the electricity sector. The third study is a normative assessment of the ethical and practical challenges of implementing an exemption for the poor in international climate mitigation agreements.

In the absence of broad-based institutions for redistribution, governments subsidize essential consumption, such as food and household energy supply, but with well

v known fiscal and environmental costs, including to climate change. However, how the benefits of the kerosene subsidy policy are distributed among the millions of kerosene users is not understood. This study formally examines these benefits for different income groups and the overall efficacy of kerosene subsidies as a redistributive policy. The study shows that households’ allocated quotas far exceed kerosene demand in rural areas, which encourages suppliers to divert kerosene to other sectors. Urban households, on the other hand, some of whose cooking budgets would double without the subsidy, supplement subsidized kerosene use with purchases in the black market. A better targeted subsidy in urban areas alone would avoid high costs of the current policy, yet avoid the impoverishment of urban users from their complete removal.

The second study assesses the distributional impacts of investing in low carbon supply in Maharashtra’s electricity sector to the extent required to meet the Indian government’s pledge in climate negotiations to reduce the economy’s carbon intensity. I examine the regulator’s leverage in rate-setting over the distribution of these incremental costs across households. Using an economic simulation model of the electricity sector and household welfare, I assess the impacts of economy-wide electricity price scenarios under different political and institutional constraints. The analysis reveals that regulators can insulate low-income households from welfare losses without trading off aggregate welfare losses as long as they can raise prices to industry and high-income households. While feasible, this pricing approach may be politically unacceptable. Mitigation may also have a co-benefit of reducing supply interruptions to the poor. These results emphasize the importance of qualifying mitigation burdens by the internal policies and institutions on which they depend.

The third study questions the adequacy of burden-sharing proposals for climate mitigation that advocate an exemption for the poor without accounting for states’ agency over the costs and outcomes of such an exemption. How to allocate the cost of this exemption, however, can complicate international agreements. Participating states face moral hazards over the choice of future baselines of the poor’s emissions. I show - using India for illustration - that the financial stakes for parties in how future growth is distributed in India can be up to tens of billions of dollars. I suggest that there is no

vi clear moral basis to make benefiting states accountable for minimizing the poor’s emissions. Getting agreement on the terms of exemption may be easier if benefiting states adopt comparative benchmarks of accountability for the poor’s emissions, but which do not infringe on particular policy choices. Furthermore, participating states should share design agreements to ensure that the poor receive the benefits of an exemption.

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ACKNOWLEDGEMENTS

Dedicated to the memory of Stephen H. Schneider (1945-2010)

I am grateful to Steve Schneider, who was my primary advisor and mentor, for encouraging me to pursue this topic and imparting the meaning and importance of interdisciplinary research. After his passing, I have been extremely fortunate to have the close guidance, open-mindedness, patience and rigor offered by Larry Goulder. Joshua Cohen was instrumental in developing my ideas in global justice. Debra Satz gave me invaluable guidance and opportunities to present my work to intimidating audiences. I am grateful for David Victor for his pragmatism and commitment to my development and for financing my fieldwork through the Program in Energy and Sustainable Development. Paul Baer has been an informal advisor and collaborator whose work served as a launching point for mine. I thank Ashok Gadgil and Kirk Smith at Berkeley for useful conversations in the formative stages. My conversations with Girish Sant of Prayas Energy group kept me assured of the policy relevance of my work. I am indebted to Gayatri Gadag, Ravi Deshmukh and Dipak Patil in Pune, India for their help with data gathering and fieldwork. I thank my research assistants, Chris Bennett, Evan Woods and Allison Fink, for their contributions to my analysis.

I am grateful to Pam Matson, Danielle Nelson and Helen Doyle for the opportunities and support I have had at E-IPER. My dissertation was made possible through funding from the Robert G. Kirby and Philip & Jennifer Arnold Satre Fellowships. Its successful completion would not have been possible without the moral support of Asha Ghosh.

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TABLE OF CONTENTS

CHAPTER 1 – CLIMATE CHANGE MITIGATION: A HUMAN RIGHTS PERSPECTIVE ...... 1

CHAPTER 2 – KEROSENE SUBSIDIES: WHEN ENERGY POLICY FAILS AS SOCIAL POLICY...... 27

CHAPTER 3 – DISTRIBUTIONAL IMPACTS OF CLIMATE CHANGE MITIGATION IN INDIAN ELECTRICITY: CASE STUDY OF MAHARASHTRA...... 56

CHAPTER 4 – IMPLEMENTING AN EXEMPTION FOR THE POOR IN INTERNATIONAL CLIMATE AGREEMENTS ...... 87

CHAPTER 5 – CONCLUSIONS AND FUTURE RESEARCH ...... 111

Appendix A - Maharashtra Kerosene Quota Allocation ...... 119

Appendix B – Detailed Results and Data Tables (Chapter 3) ...... 120

Appendix C - Long-Term Demand Response Model (Chapter 3) ...... 125

Appendix D - Household Survey Design ...... 129

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LIST OF ILLUSTRATIONS

Figure 1: Basic Entitlements – Interpretations and Metrics in Literature ...... 14 Figure 2: Per Capita Carbon Dioxide Emissions: Country Averages and Internal Distribution (Illustrative) ...... 19 Figure 3: Variation in Urban Household Kerosene Use - Mumbai, Maharashtra and India 2004-05 ...... 30 Figure 4: Cooking Fuel Shares by Income Decile – Maharashtra, 2004-05 ...... 31 Figure 5: PDS Kerosene Prices by Quantity Sold – Maharashtra 2004-05 ...... 34 Figure 6: Kerosene Use for Cooking/Water Heating – Maharashtra 2004-05 ...... 37 Figure 7: Kerosene vs. LPG Delivered Fuel Cost Comparison (2004-05 prices) ...... 39 Figure 8: Population Share by Kerosene Subsidy Benefit – Maharashtra 2004-05 ..... 43 Figure 9: Kerosene Subsidy Progressivity: Urban and Rural Maharashtra 2004-5 ..... 44 Figure 10: Kerosene Subsidy Progressivity – Nandurbar District, 2004-05 ...... 45 Figure 11: Kerosene Subsidy Quotas and Actual Use (a) Urban ...... 46 Figure 12: Households with Insufficient Kerosene Quotas – Maharashtra 2004-05 ... 48 Figure 13: Subsidy Price by Purchased Quantity – Maharashtra 2004-05 ...... 50 Figure 14: PDS Kerosene Prices, Quantities, and Transport Distances by District ..... 51 Figure 15: Electricity and Welfare Model Simulation Approach ...... 58 Figure 16: Electricity Block Tariff – Maharashtra State Electricity Board, 2004-05 .. 66 Figure 17: Residential and Industrial Price Impact Comparison ...... 75 Figure 18: Industry Group Electricity Intensities (2003-2004) ...... 76 Figure 19: Household Expenditure Electricity Intensity – by Income Group ...... 76 Figure 20: Average Residential Prices - Low Carbon Pricing Scenarios ...... 79 Figure 21: Distribution of Welfare Losses - Low Carbon Pricing Scenarios ...... 80 Figure 22: Distribution of Welfare Losses - Energy Efficiency Sensitivity for the Economic Efficiency Scenario ...... 83 Figure 23: Distribution of Welfare Losses - Energy Efficiency Sensitivity for the Equity Scenario ...... 84 Figure 24: Country Intranational Income and Emissions Distribution: 2007 ...... 93

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LIST OF TABLES

Table 1: Kerosene Consumption by Region and Market: Maharashtra 2004-05 ...... 32 Table 2: Household Kerosene Use by Function, Region and Priority ...... 36 Table 3: PDS Kerosene Price Discrimination Model Results ...... 53 Table 4: Optimal Prices – Baseline and Low Carbon Scenarios ...... 77 Table 5: Welfare Metrics – Baseline and Low Carbon Scenarios ...... 78 Table 6: Optimal Prices – Industrial Elasticity Sensitivity ...... 81 Table 7: Welfare Metrics – Industrial Elasticity Sensitivity ...... 82 Table 8: Optimal Prices - Energy Efficiency Sensitivity for the Economic Efficiency Scenario ...... 84 Table 9: Optimal Prices - Energy Efficiency Sensitivity for the Equity Scenario ...... 84 Table 10: Exemption Costs Under Alternate Development Paths in India ...... 94 Table 11: Informal Economies in Developing Countries ...... 103

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CHAPTER 1 – CLIMATE CHANGE MITIGATION: A HUMAN RIGHTS PERSPECTIVE

1 Introduction Global climate change has been called the “perfect moral storm” (Gardiner 2010). This is for good reason. Given the unprecedented nature of climate change, humans must confront a number of ethical challenges at once. Greenhouse gases (GHG) emitted from sources across the globe have accumulated in the atmosphere over centuries, to a large extent without our knowledge, causing unintended and potentially catastrophic impacts on people and biodiversity in the future. While these emissions come largely from industrialized societies, their long-term impacts fall disproportionately on poor societies, through weather-related events that are not easily traced to their causes, and with considerable scientific uncertainty surrounding their severity and timing. Furthermore, that GHG arise primarily from the use of an essential input – energy - into most human activity implies that to minimize the effects of climate change societies would have to undergo shifts in infrastructure as well as in lifestyles at an unprecedented scale, scope and pace. How will we compromise to distribute this responsibility across countries? No matter what or how much action humans take to combat climate change, these actions will have moral repercussions, for people in the future or today, and mostly likely for both.

The climate change problem presents challenges for decision-making at the individual, state and international level. The complex causal chain between human activity and climate impacts and the separation in time and space between emitters and victims makes it hard for individuals to acknowledge the threat of climate change, let alone to modify their behavior (Swim 2009). At the state level, modern societies‟ reliance on centralized sources of emissions (“collective emissions”) such as power plants and industrial facilities and dispersed sources such as automobiles necessitate the establishment of economy-wide institutions and policies to enable emissions reductions. At the international level, because the atmosphere serves as a “global commons” into which all humans‟ GHG emissions accumulate the worst impacts of

1 climate change cannot be mitigated by the actions of any one country alone. Combating climate change requires cooperation among countries, each of which have different interests in climate change policy, states of development and political power. A critical concern is that the poorest populations in the world contribute the least to, have the most to lose from, and have the least bargaining power to influence negotiations on, climate change (Sagar 2001).

As such, global climate change is arguably the most complex international policy challenge facing humanity. After over three decades of scientific research, almost two decades of international negotiations, and the publication of four reports by the Intergovernmental Panel on Climate Change (“IPCC”), over 120 countries - including the United States, European Union members, China and India - signed the Copenhagen Accord. This non-binding agreement symbolizes the overwhelming acceptance by state governments of human interference with climate.

We agree that deep cuts in global emissions are required according to science, and as documented by the IPCC Fourth Assessment Report with a view to reduce global emissions so as to hold the increase in global temperature below 2 degrees C, and take action to meet this objective consistent with science and on the basis of equity. Copenhagen Accord, December 2009

This agreement symbolizes an acknowledgement of our responsibility to prevent imposing on future generations the harmful effects of climate change that would be avoided with a maximum temperature rise of 2 degrees centigrade (“2C”).1

To achieve this target, the IPCC indicates that cumulative GHG emissions in the 21st century would have to reduce from a projected average of 670 gigatons of carbon (“GtC”) to 490 GtC (IPCC 2007). Based on current trends (“business as usual”, or “BAU”), this implies that by 2030 annual global GHG emissions would have to reduce to about half the levels that would otherwise occur (Project Catalyst 2009). The pledges made by state governments in the Cancun Agreement of December 2010

1 Stabilizing GHG concentrations in the atmosphere at 450 parts per million (ppm) would keep the odds of increasing average global temperature since pre-industrial levels by 2 degrees Celsius (“2C”) below 50 percent.

2 amount to only 60 percent of the mitigation levels required to achieve this target.2 Indeed, it has been estimated that even in the best case scenario that all the pledges are implemented, we are still virtually certain to exceed the 2C target (Rogelj, Hare et al. 2009).

Perhaps the most divisive issue in international climate politics has been the issue of how to distribute between developed and developing countries the burdens of reducing GHG emissions (“mitigation”) further to meet the 2C target. The term „common but differentiated responsibilities‟ (“CBDR”) in the UN Framework Convention for Climate Change (UNFCCC) symbolizes the intent of the signatories to distribute the burdens of responding to climate change so that industrialized countries would take the lead in reducing emissions, while the less developed countries would give priority to their development but aim to integrate climate change concerns in the future.3 In the climate ethics literature scholars have interpreted CBDR in many ways, but generally place greater responsibility for mitigation on industrialized countries (Gardiner 2004; Gardiner, Caney et al. 2010). A meta-analysis of different burden-sharing proposals suggests that under most views of equitable burden-sharing developed countries as a whole need to reduce their emissions by 25-40 percent below 1990 levels, while developing countries together would have to reduce emissions by 15-30 percent below their BAU levels (den Elzen and Höhne 2008) by 2030.4 However, even though such equity formulations have been proposed in negotiations since the first Conference of Parties (“COP”) in 1995 (Ringius, Torvanger et al. 2002), developed and developing countries have failed to have a dialogue about these equity principles, let alone reach any agreement on how to allocate mitigation responsibility.

Although equity principles may never drive political negotiations, the need for agreement on burden-sharing rules that are perceived as fair is compelling. The

2 Christine Figueres, UN Secretariat, December 20, 2010, Reuters. 3 Articles 2 and 3.1of the UNFCCC. The Convention has been ratified by193 countries, including the United States. 4 Recent proposals from scholars in China and India calculate mitigation obligations based on a per capita entitlement to cumulative historical emissions extending back to the early 20th century. These proposals would exempt China and India from any mitigation obligation until after 2030.

3 cooperation of large developing countries is essential to reach the 2C target, since almost half the future energy demand growth by 2030 is expected to come from just China and India (International Energy Agency 2008). But both countries‟ governments are unlikely to agree to mitigation obligations that are perceived as demonstrably unfair. Thus, it is also of political interest to assess fairness as one lens of burden- sharing, while also giving attention to how to make these principles practicable and how they affect the incentives for participation by state governments.

In the climate ethics literature, with few exceptions, scholars treat states as moral agents, by defining mitigation obligations for them based on aggregate indicators, such as GDP or emissions, but without accounting for heterogeneous national circumstances. This characterization would either exempt or impose mitigation obligations on large developing countries like China and India without regard for the different segments of society that have vastly different levels of development but which in aggregate terms account for comparable emissions. This has both ethical problems and risks being politically untenable. Even the few exceptions where scholars account for internal income inequality, states are treated as passive agents.

In this dissertation, I examine the implications of defining and implementing mitigation obligations for subpopulations within developing countries. I explore the ramifications of adopting one morally compelling principle of exempting the least advantaged in society from the burdens of mitigation as a minimal basis for distributing mitigation responsibilities. The central argument explored in this study is that sovereign state policies influence the distribution of mitigation burdens related to such an exemption among states. This raises new questions about the role of states that receive such an exemption in climate agreements. The object of this study is both positive and normative: on the positive side, I evaluate how state policies and institutions influence the distributional impacts of climate mitigation policies using India as a case. On the normative side, I evaluate the accountability of parties to an agreement to achieve its objectives in light of these sovereign influences. I also assess the issues that arise in getting agreement on the terms for such an exemption in international agreements.

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The remaining part of this chapter describes in more detail the motivation for choosing this principle, the gaps in literature associated with operationalizing it, and how the subsequent chapters contribute towards filling these gaps. This dissertation makes other independent empirical contributions to policy literature regarding the impact of energy policies on income distribution in India. These are also described below.

2 Scope of this Study

2.1 Focus on Intra-generational Equity For convenience, scholars make a distinction between the ethical question of what level to stabilize global temperature, from the related but distinct ethical problem of how to distribute the sacrifices required to achieve this given level of temperature stabilization among the current generation of people.5 The first is an inter- generational equity issue that is viewed primarily as a question of how to balance the interests of future and present generations of people. Because of the lagged effects on climate of accumulating GHG in the atmosphere, global temperature would not stabilize until after 2050, so only future generations would benefit from abatement. But meeting this target would require making sacrifices today to shift to a low-carbon infrastructure and prevent the „lock-in‟ of carbon-intensive sources of GHG, which typically have lives of 10-50 years. Strictly speaking, future generations may have to make sacrifices to maintain a steady-state level of GHG emissions. However, maintaining safe levels of emissions in perpetuity may be less burdensome once society has invested in low carbon technologies and climbed the learning curve of putting in place and adjusting to a low carbon economy.

5 This separation is driven by practical considerations rather than moral ones. The same moral principles apply in resolving both inter-generational and intra-generational issues. The choice of a particular climate stabilization level may well have a bearing on the choice of an equitable distribution of mitigation burdens today, and present moral trade-offs. If, for example, we chose to stabilize emissions at 350 ppm, future generations would face less severe burdens from climate impacts, but the increased mitigation requirements would, ceteris paribus, impose greater risks of burdens on the poor today. Both issues therefore would merit simultaneous consideration purely on moral grounds.

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For the purposes of this work, I take this inter-generational equity issue as resolved, at least in aspiration. There remain questions on the one hand about whether 2C is enough,6 and on the other hand whether 2C is too burdensome or even feasible. Without knowing the level to which states actually commit, what matters to this work is that the target is ambitious enough that large developing countries‟ participation is unavoidable for its attainment. The practical implications of this assumption are discussed later. I focus strictly on the intra-generational problem of how to distribute the costs of meeting such a target.

2.2 Focus on Mitigation In the climate justice literature, the intragenerational burden-sharing issue addresses distributive justice for both mitigation and adaptation, and often together. The distribution problem for adaptation is concerned with how to distribute the costs of compensating victims of unavoidable climate change impacts. These impacts are likely to affect the lives and livelihoods of millions of vulnerable populations in the equatorial belt who do not have the means to adapt to these changes. However, mitigation is a separate ethical problem from adaptation, even if common principles may apply in their resolution (Vanderheiden 2009).7 I focus only on the mitigation problem.

2.3 Focus on Energy-related Carbon Dioxide (CO2) Emissions I focus specifically on the distribution of burdens associated with mitigating carbon dioxide (CO2), which is the GHG with the longest life, fastest growth, and as a consequence the greatest cumulative contribution to climate change.8 With the

6 While the 2C target has become the marker for dangerous interference, this is a subjective judgment that was ultimately determined by political rather than scientific or moral considerations. The governments of small island states in the Asia Pacific advocate for a 1.5C threshold to avoid the risks of sea level rise on these islands. 7 For example, a stronger case can be made for counting historical emissions in determining peoples‟ liability for compensating victims of climate change than the case that can be made for counting historical emissions in determining mitigation burdens, even though reasonable arguments have been made in support of historical emissions in both distribution problems. See Vanderheiden, S. (2009). Atmospheric Justice: A Political Theory of Climate Change. New York, Oxford University Press. 8 Carbon dioxide accounts for 60 percent of the total increase in radiative forcing, compared to 20 percent from methane. This is because although CO2 has a lower global warming potential (1:25), it has

6 exception of Brazil, CO2 emissions account for two-thirds or more of the GHG emissions of the 20 largest contributors to global GHG emissions. Over 90 percent of

CO2 emissions come from burning fossil fuels, which makes the task of reducing emissions intricately tied to how we use energy.

3 Support for Defining a Moral Minimum The notion of exempting a certain category of people from mitigation burdens derives from a human rights principle of a universal human entitlement to a minimal set of „goods‟. This view finds support in the climate ethics literature, but has its roots in the broader global distributive justice and has supporting international institutions. I discuss these motivations below.

Basic rights are the morality of the depths. They specify the line beneath which no one is allowed to sink. Henry Shue (1999), Basic Rights.

The notion of a moral „minimum‟ occupies considerable space in annals of political philosophy. Henry Shue‟s statement above characterizes the spirit of a moral threshold. What does respect for human dignity demand that all humans have regardless of their culture, nationality, location in history or place? More importantly, who has duties to uphold these claims? Of interest here and in the global distributive justice literature are the types of duties that such claims raise for states towards people in other states. Two types of duties of external states are typically contemplated: duties to help realize people‟s claims and to protect them from infringement by their own state governments (“positive duties”); and duties to respect, or not infringe on, other states‟ abilities to fulfill these rights for their own people (“negative duties”). While there is considerable debate among philosophers as to whether universal human rights exist at all, the existence of negative duties is relatively less controversial than positive duties. That is, notwithstanding the debate over what entitlements deserve immunity,

a longer lifetime (5-200 years: 8-12 years).CO2 emissions are growing at over 1 percent per annum globally, and at almost 5 percent in developing countries, while methane emissions are constant or declining (World Resources Institute, cait.wri.org).

7 there is little justification, except under extenuating circumstances, for knowingly infringing on others‟ basic entitlements.9 As discussed above, the goal of establishing a moral minimum for mitigation agreements is to apportion mitigation responsibility in a manner that does not infringe on the poor‟s basic rights. Thus, in this context, where what is at issue is only whether parties to a mitigation agreement ought to respect such a moral threshold, a moral minimum has considerable theoretical appeal.

3.1 Human Rights International Institutions A number of international institutions exist that provide some legal recourse for international human rights violations. The most relevant one for the purposes of climate change mitigation is the International Covenant on Economic, Social and Cultural Rights (ICESCR). The ICESCR has been signed by all the major economies including the United States, China, European Union, India, Brazil and South Africa. The ICESCR includes the right of people to be “free from hunger” (Article 11.2), the right to the “enjoyment of the highest attainable standard of physical and mental health” (Article 12.1), and to the “right of everyone to an adequate standard of living for himself and his family, including adequate food, clothing and housing, and to the continuous improvement of living conditions” (Article 11.1). The Covenant obligates Parties to “at the very least...ensure the satisfaction of minimum essential levels” of economic, social and cultural rights.10

The value of the ICESCR is to lend moral and political support to the justice concerns posed by mitigation burdens, rather than to provide a legal mechanism to uphold claims related to mitigation. Making a legal case for a human rights violation from climate change is fraught with controversy and challenges, such as demonstrating causation in proving injury, and identifying defendants. Particularly with climate mitigation, assessing rights violations requires an evaluation of development policies, which international law does provide a clear means of doing (Humphreys 2010).

9 Such extenuating circumstances may be where certain rights have to be infringed upon in order to prevent other, more serious, rights violations. 10 ICESCR, General Comment 3, The Nature of States Parties‟ Obligations, UN Doc. E/1991/23 (Dec. 14,1990), ¶ 10, cited in Bodansky (2010).

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However, if mitigation burdens can be credibly framed as a human rights issue under the ICESCR, this can be used to capture public opinion (Bodansky 2010). Politicians would be hard pressed to publicly oppose a position that advocated respecting human rights, particularly one that demands only forbearance from causing harm. Indeed, politicians have de facto supported this principle by not demanding the participation of the poorest countries in mitigation agreements (Ringius, Torvanger et al. 2002). In comparison, the principle of historical responsibility, which is one of the contentious equity principles that divides developed and developing countries, carries sufficient moral ambiguity on grounds of past ignorance to climate change, that US negotiators have publicly dismissed the principle outright.11

3.2 A Unifying Thread in Climate Mitigation Burden-Sharing Due to the unique nature of global climate change, the mitigation burden distribution problem has been viewed through many lenses of fairness. Each of these views is founded on different principles and, when operationalized, can lead to different burden-sharing outcomes. A moral minimum represents a relatively uncontroversial principle that constrains, but is consistent with, the application of these other distributive principles.

The mitigation problem has been viewed at once as one of distributing: scarce resources (to the atmosphere); responsibility (for pollution); and burdens (primarily, but not only, financial). In the resource-sharing view, global climate change puts limits on the amount of CO2 emissions that humans can safely emit. The carbon absorption capacity of the earth can be thought of then as a “global commons” with scarce capacity that has to be distributed, so that people have an equal right to pollute up to the total safe level of CO2 emissions (Agarwal 1991; Jamieson 2001; Vanderheiden 2009). States therefore have rights to emit GHG in proportion to their population share of the maximum allowable global emissions (the issue of state vs. individual entitlements is discussed later).12 States then have a responsibility to mitigate all but

11 “U.S. Negotiator Dismisses Reparations for Climate”, New York Times, December 9, 2009. 12 In practice, emissions rights could represent a financial claim to the value of the rights that can be traded rather than used.

9 their entitled emissions. This view mirrors other moral theories that view global economic inequality as arising primarily from an arbitrary and unequal distribution of natural resources (Beitz 1979). While drawing some support from such theories, the per-capita emissions view seems to represent a kind of resource fetishism, by assigning value to what is only a means to an end (Caney 2009). Further, as the per capita emissions allocation is derived from a scientific limit, there is no guarantee that this allocation protects any fundamental human interest (Hayward 2007), particularly since the benefits that flow from it – cheap energy - vary widely based on people‟s fuel endowments. Thus, this view begs the question of why an international climate burden-sharing regime should isolate this one resource for redressing unequal access, when the benefits that such a right would provide – energy – can be obtained from other resources, such as solar energy.

The other two approaches are both principles of proportion,13 reflecting the objective of treating “comparable people comparably”. Mitigation costs should be apportioned based on either the responsibility for causing the problem, or the capacity to bear the burden of the problem. In the responsibility-based approach, since global warming increases roughly in linear proportion to cumulative CO2 emissions, those who emit more are more at fault for causing global warming, and therefore ought to bear proportionately more responsibility for mitigation (“polluter pays”). This view is backward-looking, since past and present emissions of a particular GHG that accumulate in the atmosphere contribute equally to climate change.14 However, the responsibility approach is problematic when applied to individuals rather than states, because historical emissions are not obviously the responsibility of individuals who happen to have been born in the same state (Caney 2010). This view also penalizes states for their fuel endowments by putting at risk the basic needs of people who can

13 As in Aristotle‟s dictum: “what is just is what is proportional, and what is unjust is what violates that proportion”. Aristotle, Nicomachean Ethics: Book V: Ch.3. 14 This responsibility may extend only as far back as the point in time prior to which people can claim that ignorance about climate change absolves them of any liability for the harm caused by prior emissions.

10 ill afford to bear mitigation burdens, and who may have no control over their emissions.15

The capacity view, on the other hand, is forward-looking. People should bear mitigation costs in proportion to some measure of their “ability to pay”, such as income or wealth. This is because CO2 emissions derive from the combustion of fossil fuels, whose use is ubiquitous in the global economy. Mitigation therefore imposes burdens on people by inducing lifestyle changes, shifts to more expensive low carbon energy sources, or emissions reductions from existing energy sources. Since people ultimately care about their welfare and not emissions, the problem should be viewed as one of distributing the burdens on human welfare that arise from mitigation. However, the capacity view penalizes those who may have low emissions due to efficient energy use or less carbon-intensive lifestyles.16 And more importantly here, the capacity approach gives priority to the poor, but doesn‟t proscribe imposing mitigation burdens that may cause harm.

Both the principles of proportion thus have their respective merits and problems (Caney 2010). These tradeoffs are somewhat irreconcilable, and subject ultimately to political, rather than ethical, resolutions.17 But all three approaches to different degrees risk imposing mitigation responsibility on the poor. This gives cause to define a minimum inviolable threshold of human well being upon which mitigation burdens should not impinge (Shue 1999; Caney 2009). This threshold thus serves to circumscribe the scope of applying burden-sharing principles, rather than provide an alternative to them. Note that these distributive principles are also consistent with a moral minimum – any of them can be applied to divide mitigation burdens among the emissions and people that are not exempt by virtue of this minimum.

15 See Section 3.1 - Brazil and South Africa have almost identical average GDP and income inequality, but Brazil has a quarter of South Africa‟s carbon dioxide intensity (and half its GHG intensity) due to their reliance on hydro and coal respectively. Imposing responsibility-based mitigation burdens on both countries imposes greater risks to the interests of South Africa‟s poor population. 16 For example, the United States and Hong Kong have comparable average GDP, but Hong Kong has almost 40 percent lower energy and carbon intensity (which therefore is not due to fuel endowments). 17 One burden-sharing approach in fact combines both in proportion that can be chosen by decision- makers (Baer et al 2008).

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Thus, a moral threshold can be thought of as a unifying and morally compelling thread that runs across these burden-sharing approaches. Below I expand on how this principle serves as a minimal principle of distribution, before I turn to the content of such a threshold.

3.2.1 What does ‘minimal’ mean? There are many senses in which a moral threshold of exemption is minimal in a mitigation burden-sharing framework. The most important sense is that this principle has lexical priority over all other distributive principles that are invoked to determine a fair allocation of mitigation burdens (Caney 2009). As discussed above, this threshold is inviolable, and therefore absolutist, 18 in the sense that under no circumstances should people bear any mitigation burdens that compromise these rights.

The second sense in which a moral threshold is minimal is that it alone is not sufficient to specify a complete allocation scheme for mitigation burden-sharing. This principle only defines an exemption from mitigation, but says nothing about how burdens should be distributed among those who are not exempt. One might think of a „default‟ approach that eschews any principles of „fair‟ distribution, and treats all present and future emissions equally. In such a case, states and/or individuals would reduce their own emissions based on causation, or proportionately. If the exemption were incorporated as the only equity consideration, the resulting allocation scheme can be thought of as the least demanding claims that can be made by developing countries, in principle. As noted earlier, the content of this claim may still be financially demanding, depending on the definition of such an exemption.

Lastly, because of its theoretical appeal and importance, respect for a moral threshold is the least of justice considerations that a global climate mitigation regime ought to incorporate, if it has to eschew all other considerations. When viewed with this perspective, a moral threshold for exemption from mitigation may serve as a common

18 To be clear, it is not absolutist in the context of all human rights. That is, basic needs do not necessarily have lexical priority over the right to life and security.

12 ground for agreement between major economies, if cooperation between developed and developing countries depends on the incorporation of justice concerns at all.

I now turn to the content of an exemption threshold.

3.3 Moral Threshold – Equality of What, and How Much? An inviolable moral threshold “in principle” risks being facile without some claim about its content. Even the practical significance of an exemption threshold in a climate agreement rests on how expansive a guaranteed minimum ought to be, rather than on whether one ought to exist at all. At the same time, the „Equality of What‟ debate – what features of the human condition should count in basic entitlements - is larger than the domain of climate change. This study does not engage in that debate. However, it is possible to provide at least reasonable bounds for a moral threshold for which there is sufficient support in literature, keeping in mind that this support need only apply to the negative duty of non-infringement.

The literature on what constitutes fundamental human entitlements is extensive. Definitions vary in how they have been conceptualized and in the range of their constitutive elements (Figure 1). Among philosophers, in their „thinnest‟ form it has been argued that a minimal set of universal rights comprises rights to only physical security. Henry Shue defines subsistence rights as additionally comprising economic security, on which human‟s survival also depends (Shue 1980). Shue argues that these basis subsistence rights have lexical priority over all other types of rights, because other rights cannot be enjoyed by people unless they can subsist. In the climate ethics literature, Shue indirectly supports a subsistence-based moral threshold by differentiating „subsistence emissions‟ from luxury emissions (Shue 1993).19

A number of scholars view human rights as instrumental to ensuring a quality of life beyond mere subsistence, but do not dwell on the meaning of „decency‟(Buchanan

19 Simon Caney (2010) points out that Henry Shue does not use the language of rights despite being an advocate of basic rights. This may be to avoid alienating opponents of human rights formulations in general. However, the content of his formulation of subsistence emissions derives unambiguously from subsistence needs, even if he frames them in terms of emissions.

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2004; Hayward 2007; Caney 2009). Simon Caney, more recently, casts human rights as claims to a decent standard of living, or to the emissions that are required to enjoy decent living conditions. Nussbaum and Sen conceive of basic entitlements as constituting capabilities or opportunities for human function rather than a set of goods or services.

Figure 1: Basic Entitlements – Interpretations and Metrics in Literature

Note: Marks indicate the metrics used for different definitions of entitlements in literature.

There is also a viewpoint that frames basic entitlements as development rights, which include economic, social and political entitlements to individuals. This approach is distinguished by its emphasis on a collective entitlement to a process of change that leads to the progressive realization of the individual rights (Andreassen 2006). Although development rights are a relatively undeveloped and unexplored concept, the exploration of the importance of collective processes for realizing individual rights is unique and important. The problem is that there is little theoretical guidance as to what such a collective entitlement should entail, and how much energy (and by extension emissions) that entitlement should include.

Among this range of definitions, it is plausible to infer that subsistence needs are a common and minimal set of basic rights. No other functions or liberties can be enjoyed without subsistence. Then, subsistence can be thought of the most minimal candidate for a moral minimum. However, what additional entitlements beyond subsistence should count is an open question. Broader entitlements related to political

14 rights or capabilities are relatively abstract. However, there is wide support for including some measure of decent living standards, even if their specification is subject to some bargaining.

A few burden-sharing proposals incorporate an exemption for emissions within countries. They indeed demonstrate the wide range of possible interpretations of a moral minimum. Muller et al and Chakravarty et al estimate that a 1 ton per capita threshold provides for subsistence needs. On the more inclusive side, Baer et al in Greenhouse Development Rights base their exemption threshold on an entitlement to development rights, which they quantify at $20/day per capita. This represents the type of „upper bound‟ mentioned above that has some normative appeal but is hard to justify as a specific number (Baer 2008).

It is therefore important, but difficult, to objectively define a more expansive upper bound for a reasonable threshold than mere subsistence. Though seemingly obvious, this observation has serious implications for how an exemption threshold might be implemented in practice. Without a normative basis, such a threshold may have to be determined through political negotiations. Under this assumption, I set aside the matter of the level of the threshold in this study.

I turn next to how to measure such a threshold.

3.3.1 Metric for a Moral Minimum Income is a frequently used and a practical choice for a metric for an international exemption threshold, despite its known limitations as an indicator of human well- being (Figure 1). There is a well-known tension between inclusiveness and their measurability in defining an indicator of human well being and its constituents. Indicators of subsistence needs, such as calorie intake, are reasonably accurate. The Human Development Index (HDI) comprises income, life expectancy and literacy, but the latter two are crude proxies for health and education. Among the more inclusive measures is the Multi-dimensional Poverty Index (MPI), which contains 10 indicators of health, education and living standards, including access to water, sanitation and

15 electricity.20 However, the added dimensions complicate measurability and inter- personal comparisons. For instance, the electricity access indicator does not capture actual energy supply, which is a more accurate indicator of the quality of life conferred by electric service. Further, a composite index implicitly allows trade-offs between potentially incommensurable indicators, such as life expectancy and electricity access. Such indices, while more inclusive, present a potentially misleading standard of objectivity in comparing living situations in different cultural and geographic contexts.

While the development of these indicators has been motivated to a large extent by the limitations of income as an indicator of living standards, they have yet to match the benefits of income as a commensurable indicator across countries. Income provides a universal measure by which to assess the value of disparate goods and services that reflect people‟s living standards, by measuring the costs of these services in market transactions. Where market transactions fail to accurately reflect people‟s living conditions, techniques have been developed to impute prices for non-market services and externalities, and to adjust price indices to reflect different purchasing powers (Stiglitz 2008).

As a practical matter, income also correlates with these multidimensional indicators, at least at low levels of development, and with emissions. Income exhibits a relatively predictable correlation to carbon dioxide emissions at an aggregate level, both across and within countries. A National Academy of Science paper shows that emissions elasticities of income across and within countries range from 0.7-0.9. With multi- dimensional indicators, the body of research on these metrics is too limited to provide either an understanding or actual data of their relationship to emissions in different countries.

To summarize, exempting the emissions associated with human‟s most basic living conditions from people‟s mitigation obligations is a morally compelling justice

20 The Oxford Poverty and Human Development Initiative (http://www.ophi.org.uk/policy/multidimensional-poverty-index/)

16 principle that ought to have priority over other principles that might govern how mitigation costs should be distributed. For a threshold to have moral and practical significance in an international climate change agreement but not escape credulity as a minimum, such a threshold must be defined to include more than mere subsistence but not extend beyond standards for decent living conditions. However, justifying a particular basket of goods or a threshold level as a moral minimum is not important for the purposes of this study. For the purposes of policy, the metric of income provides a reasonable compromise in quantifying a basic minimum between the needs of inclusivity and flexibility on the one hand, and the need to translate such a threshold into an emissions exemption. I now turn to the challenge associated with operationalizing a moral threshold in a climate mitigation regime.

4 Limitations of a Statist View of a Moral Minimum The principle of a moral minimum challenges presumptions in climate ethics and policy about the beneficiaries and agents of burden-sharing arrangements. With few exceptions, the discourse on climate equity assumes states to be agents of a burden- sharing arrangement, not just as the parties who would implement the terms of an agreement, but the agents whose interests the agreement would be designed to protect. This assumption has also been made among scholars who think that burden-sharing arrangements should allow for poor peoples‟ development needs (for instance, by exempting poor countries from a climate agreement).

As such, scholars have predominantly proposed exemption thresholds that apply to states. These entail the notion of „graduation‟ – that countries abstain from taking steps to reduce emissions until they reach some threshold indicator, such as average GDP or average per capita emissions, or a combination of the two (Höhne 2006; Frankel 2007; Michaelowa 2007).

This statist approach to climate equity is compelling from a practical standpoint. First, it may be a reasonable simplification to account for disparities in wealth across countries if the inequalities within countries are of a second order. Second, it may be

17 prohibitively burdensome to account for and verify internal socioeconomic indicators in structuring and implementing agreements.

These considerations are important, if true. However, they are as yet unexplored. How much would a statist approach distort the distribution of burdens, and what is financially at stake? For large developing economies that have comparable total emissions from both wealthy and poor populations, the case for a statist approach seems weakest. Consider, for example, the emissions of the populations earning below and above the average GDP in China, India, Brazil and South Africa (“BASIC countries”), who together contributed 60 percent of non-Annex 1 countries‟ GHG emissions in 2005. In aggregate terms, the CO2 emissions from those who earn more than the countries‟ average GDP are comparable, with those earning less contributing over 40 percent of total emissions in all these countries.21 But in per capita terms, the higher income group comprises a third or more of the population but have average per capita emissions that are more than double that of the population who earn less than average GDP. This does not even represent the full spread of emissions (and income) inequality within these countries. In India, for example, in 2003-04 the top 3 percent of the population had CO2 emissions of at least 4 tons per capita, which was four times the country average.22

If, hypothetically (Figure 2),23 a burden-sharing regime were established where countries that had average per capita emissions below the world average (~4.5 tons) were exempt from mitigation, Brazil and India would be exempt, but approximately 50 million people in Brazil and 35 million people in India who emit more than the

21 This figure has been calculated on the basis of an empirically validated observation that income distribution best resembles a log-normal distribution, which is uniquely specified by the average GDP and Gini coefficient. See Chapter 4 for details on this. Data sources include: for income and population, International Monetary Fund (2008); CO2 emissions, US Energy Information Administration (2008); Gini coefficient, World Bank (2005) 22 According to Parikh et al (2009), ten percent of the urban population in India (~360 million) has average carbon dioxide emissions of about 4 tons/capita. However, this is an underestimate, because these data are based on household consumption expenditure, which the upper income groups are known to understate in surveys. 23 Variations of this rule have been proposed in the literature. See Höhne, N., Michel den Elzen, and Martin Weiss (2006). "Common but differentiated convergence (CDC): a new conceptual approach to long-term climate policy." Climate Policy 6: 181-199.

18 world average would also be exempt. On the other hand, since China‟s emissions exceed the world average, even with such a general exemption level 800 million people in China would risk being exposed to mitigation burdens that the country would have to adopt if mitigation efforts were not well targeted.

Figure 2: Per Capita Carbon Dioxide Emissions: Country Averages and Internal Distribution (Illustrative)

30

25

20 USA (tons) 2 15 Russia 10 Japan EU (27)

Per Capita CO Capita Per China 35 million 5 World Average Middle East

India Brazil Africa 0 800 million

Note: Country emissions distributions show population ordered in increasing per capita CO2 Data Sources – See Footnote 21.

Thus, it seems that for large developing economies in particular,24 it would be unreasonable to either exempt the entire country from mitigation obligations, or to impose mitigation burdens without adjusting for an exemption for the poor. For these countries, a statist approach would either arbitrarily impose mitigation burdens on some people because of their location or ignore important risks imposed by higher energy prices and other mitigation burdens on poor populations.

24 This issue is not unique to developing countries. Approximately 10 percent of the US population is below the official poverty line, which implies that they have low or even negative disposable income. Their living conditions may be better than the poor in developing countries, but they may not be in position to bear any mitigation burdens.

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5 Implementing a Mitigation Exemption within States – Contributions of this Study Imposing mitigation obligations on states that are intended for only subpopulations raises new positive and normative questions related to the implementation of these obligations. On the positive side, since mitigation obligations are intended for only those above a poverty threshold, to what extent would mitigation impacts fall on the poor under existing institutional conditions within developing countries? This dissertation examines specifically the influence of state policies over the distribution of these mitigation impacts using two cases in India. These studies are the focus of Chapters 2 and 3, and are discussed in more detail below (Section 5.1). The fact that state institutions are not accountable under international agreements but can influence the outcomes of these agreements raises normative questions about what obligations states that receive an exemption should have in implementing such agreements. This second question is the subject of Chapter 4, and is discussed in more detail below in Section 5.2.

5.1 Distributional Impacts of Energy Policies in India (Chapters 2 and 3)

There is still a lack of empirical evidence on the magnitude and direction of the interdependence and interaction of sustainable development and climate change, and [their] equity implications… New research is required that studies the linkages between climate change and national and local policies [emphasis added] Technical Summary, Working Group III, IPCC, 2007.

Climate mitigation in the energy sectors affects households predominantly through the increased costs of energy services. Prices may increase as a result of shifts towards more expensive technologies in the supply, delivery or consumption of energy, or as a result of policy instruments, such as energy taxes (or the removal of subsidies) that aim to discipline energy consumption. It is well known that in developing countries energy subsidy policies and the state-owned bureaucracies that deliver energy services to households serve many social and political objectives, such as redistributing income

20 and meeting the needs of special interests (Victor 2007). The distributive impacts on households of mitigation policies in energy depend, therefore, on these baseline service conditions and on how governing institutions implement mitigation policies in conjunction with other social objectives. The novel approach of the studies in Chapter 2 and 3 is to characterize these baseline conditions and quantify the distributional impacts of these institutions. Both studies pay particular attention to the leverage policymakers have to influence these impacts through pricing policies. I investigate the distributional impacts of two energy policies being considered or implemented in India, both of which are being pursued for reasons not related to climate mitigation but have the effect of reducing India‟s carbon intensity: removing the kerosene subsidy and investing in low carbon electric supply.

In Chapter 2, I formally evaluate the impacts of the kerosene subsidy on income distribution, its efficacy as an instrument of redistribution, and the causes of misallocations of subsidies. This study provides new insights into the different impacts of policy design and implementation failures on the distributional benefits of subsidies. I also reveal a previously unrecognized phenomenon of price discrimination and rent extraction by private licensees who distribute subsidized kerosene through the Public Distribution System.

Chapter 3 contributes to the literature on climate mitigation policy impact analysis in India. No study has as yet examined the impact of economy-wide increases in the prices of goods and services that result from undertaking mitigation in the electricity sector. I develop a simulation model of the electricity sector and household welfare in the state of Maharashtra, to examine the leverage policy makers have over the distribution of future mitigation costs. This study is also unique in that the simulation incorporates the entrenched practice of load rationing to manage supply scarcities and households‟ responses to outages. It therefore provides the most realistic simulation of sectoral conditions among studies of the Indian electricity sector.

5.1.1 India as a Case Study India is of critical importance in climate equity by virtue of the sheer number of people living in poverty. Among the large developing countries that are candidates for

21 the types of within-country exemptions discussed here, India has the largest number of poor people of any country, with a third of the world‟s poor and a third of the world‟s malnourished children. China has a comparable population and has comparable levels of inequality, but has achieved far more success in providing basic minimum living standards. Thus, the prospect of climate mitigation exacerbating poverty is a less pressing political concern in China than in India. Brazil and South Africa would also serve as good candidates for similar analyses, but the financial stakes are orders of magnitude less than in India, making their cases relatively less important in an international agreement over burden-sharing. In fact, one may ask, given that the overwhelming majority of Indians are poor (Figure 2), why isn‟t it reasonable to take a statist view and exempt India altogether? The premise of this study is that only countries with substantial rich and poor populations generate the moral dilemmas presented here. However, India does have a sizeable and growing wealthy population. Households earning more than $2/day (PPP basis) spend more than 40 percent of total household expenditure in India, even though they comprise 25 percent of the population. Further, the aggregate emissions from those earning more than $2/day are comparable to South Africa and Brazil simply because of their sheer numbers. India has a growing consumerist middle class comprising over 200 million people, whose consumption could pose serious threats for climate change if measures are not taken towards low carbon development.

Despite this empirical focus on India, the arguments and analytical approach used in this study can be applied to the BASIC countries, and also to other developing countries, such as Indonesia, Pakistan and Nigeria. Though these countries currently have predominantly poor populations, if they follow similar development trajectories as the BASIC countries, they may well develop „top-down‟, in the sense that they alleviate poverty while also creating significant wealth among the non-poor.

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5.2 Implementing a Moral Minimum in International Climate Agreements (Chapter 4) In Chapter 4, I discuss the ethical and policy implications of implementing an exemption for the poor in climate mitigation agreements. The issue I grapple with is that the outcomes of such an agreement depend on the policies and actions of state institutions that have no obligations under international agreements. This study addresses two specific aspects of this moral hazard, related to the enforcement and design of such an exemption for the poor.

5.2.1 Enforcing an Exemption Beneficiaries of an exemption in a developing country may not get the benefits of an exemption granted to a state on their behalf due to institutional corruption or weak capacity. Who should be accountable for enforcing the exemption, if anyone? Chapters 2 and 3 illustrate the enforcement problem in some detail. In Chapter 4, I discuss the ethical obligations of signatories to an international agreement to ensure that the poor receive the benefit of such an exemption. I also provide some guidance regarding how such an exemption should be structured in climate policy in light of these considerations.

5.2.2 Designing the Terms of an Exemption States can influence the future distribution of income, and therefore emissions, around a threshold through their development policies. Sovereign policies can therefore change the total level of exempted emissions, which in turn influences the costs that everybody else bears to support this exemption. If this is indeed the case, parties to such an agreement would have an interest in designing the terms of such an exemption to minimize their respective mitigation burdens, which could undermine the objectives of the agreement.

These moral hazards raise an important question regarding the distribution of mitigation burdens that has not been asked as yet in literature: how should the costs of exempting the poor be distributed between benefiting states and other parties to a mitigation agreement that incorporates such an exemption? Other relevant proposals

23 implicitly assume that these costs would be born equally by all the non-exempt members of parties‟ to an agreement. However, given that states‟ sovereign policies can influence how these costs are distributed, this assumption demands further exploration and justification.

On the practical side, I investigate how these moral hazards would affect parties‟ interests in getting agreement on the terms of such an exemption. I quantitatively assess what is financially at stake for parties to an agreement in the influence of states‟ policies over the burden of exemption. I suggest preliminary directions for climate policy in how to structure and enforce an exemption in a manner that surmounts these potential obstacles.

5.3 Precedents in International Agreements With developing countries‟ increasing participation in international trade and environmental treaties, the influence of these international institutions on poverty has received much attention (Chen 2005; Clapp 2010). While there are many parallels between previous international agreements and a future climate mitigation agreement in terms of their impacts on poverty and sovereignty, the remedies that have been sought and implemented in other realms have limited applicability to the case at hand. In particular, although countries have obtained exemptions from particular provisions of trade agreements, there are no precedents for exempting poor subpopulations in large developing countries as is contemplated in the climate ethics literature and in this study.

Both trade and environmental agreements present numerous pathways by which poverty can be exacerbated, some of which are similar in outcome to some of the impacts of climate change mitigation – such as to increase the prices of essential commodities, such as energy, food and medicines.25 As such, these agreements raise

25 Another important pathway is through livelihood impacts, such as from reduced global demand for products whose production support poor livelihoods, or by through policies that directly discourage such production in favor of other products and services.

24 equally important claims of harm to the poor‟s basic needs as would a climate mitigation agreement.

To avoid such impacts, among other motivations, there have been a few cases where countries have sought exemptions from certain obligations under trade agreements. In these cases, trade restrictions apply in specific sectors, but they are typically enforced for countries as a whole. For instance, in the Trade-related aspects of Intellectual Property Rights (TRIPS) under the World Trade Organization (WTO), least developed countries have obtained exemptions from particular provisions related to drug patents that would raises drug prices.26 WTO provisions also allow countries to restrict food exports to ensure domestic food security, and to restrict imports that do not comply with internal health and safety standards.27 While it is likely that the harmful impacts of higher drug prices, less food supply or unsafe imports may fall only on certain populations, and therefore do not necessarily justify absolute exemptions for states, political negotiations have not developed to account for such intranational inequities.

International environmental treaties have also raised complex issues regarding the claims of the poor to local environmental resources that have global benefits and to freedom from the burdens of restricting the exploitation of these resources. Here too, scholars have recognized the disconnect between the design of environmental agreements between states and the multiple subnational agents that affect or are affected by the environmental resources that these treaties are designed to protect (Herring 1999). This is best illustrated in the case of the Montreal Protocol, which is not only seen as one of most successful examples of resolving international conflicts over the protection of a global environmental resource (stratospheric ozone) but is also the most similar international environmental problem to climate change, since it involves a “global commons”. While China and India were provided compensation by developed countries in exchange for adopting restrictions on chlorofluorocarbons, Herring points out that the Montreal process in India was restricted to a narrow

26 Article 6, Transitional Arrangements, in the TRIPS Agreement. 27 General Agreement on Tariffs and Trade (GATT), Article XI, and the Sanitary and Phytosanitary Agreement under the WTO respectively.

25 governing elite, and the obligations and costs within India of compliance were not evaluated before or after the treaty.

Thus, the need to examine subnational distributional impacts of states‟ participation in international agreements seems important and neglected in literature. International agreements in other domains may well benefit from, rather than inform, the design and implementation of exemptions for the poor in climate mitigation agreements. There may be other lessons that can be learned from the experiences of previous international agreements, such as how to design a fair negotiation process or monitor compliance. However, drawing these lessons would require a more comprehensive comparative analysis of international agreements, which is beyond the scope of this study.

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CHAPTER 2 – KEROSENE SUBSIDIES: WHEN ENERGY POLICY FAILS AS SOCIAL POLICY

1 Introduction Energy subsidies have attracted renewed attention with the urgency of climate change. Historically, because broad-based institutions that enable direct cash transfers, such as income tax (Piketty 2009), are lacking in many developing countries, household fuel subsidies often serve as instruments of redistribution (Komives 2005). In India, the high fiscal and environmental costs of these subsidies have been well documented (Gangopadyaya 2005; Komives 2005). Economists estimate that kerosene subsidies, for example, carry a fiscal burden of $4-6 billion per year, and efficiency losses of $1- 2 billion per year (International Energy Agency 1999; Morris 2006). Kerosene use 28 also accounts for over 18 million tons of CO2 emissions annually, or over 1.5 percent of the country‟s CO2 emissions.

The Indian government is now considering phasing out kerosene subsidies, and eventually replacing them with conditional cash transfers (Parikh 2010). Notwithstanding the theoretical merits of direct cash payments, the feasibility, timing and merits in practice of such an ambitious identification system are uncertain and disputed (Dreze 2010).

Though kerosene subsidies are known to be poorly targeted, their distributional benefits are not well understood. Over 800 million Indians use kerosene for lighting, of which 200 million across income groups also use kerosene also as a cooking fuel.29 Suppliers and distributors divert 40-60 percent of kerosene upstream in the supply chain to other lucrative markets such as transportation,30 forcing some households to purchase kerosene in the black market (Morris 2006). The government draws support

28 Assuming emissions of 2.5kg/liter, from ~7.3 billion liters of annual consumption, based on household consumption data from National Sample Survey 2004-05 29 Author calculations using National Sample Survey of Consumption Expenditure 2004-05, discussed in Section I. 30 In transportation, kerosene is used as a cheap fuel substitute to diesel. In construction, kerosene is used in making tar for paving roads.

27 for phasing out the kerosene subsidy in part from the finding of an expert committee that the income shocks on poor households on average would be fairly small (Parikh 2010). However, these averages mask the distribution of benefits.

Even though kerosene subsidies are known to be poorly targeted, their distributional benefits are not well understood. Suppliers and distributors divert 40-60 percent of kerosene upstream in the supply chain to other lucrative markets such as transportation,31 forcing some households to purchase kerosene in the black market (Morris 2006). The government draws support for the phase-out of the kerosene subsidy in part from the finding of an expert committee that the income shocks on poor households on average would be fairly small (Parikh 2010). However, these averages mask the distribution of benefits.

The World Bank finds that in general quantity-based utility subsidies tend to be regressive because their use increases with income. However, these do not apply to kerosene, whose use does not correlate well with income. One study of fuel taxation in India finds that the direct benefits from household kerosene use are progressive (Datta 2010). However, the study offers limited detail on underlying regional differences and causes. Further, progressivity alone may not justify public spending on subsidies. Other redistributive policies may have higher impacts on poverty and cost less to deliver.

In this paper I formally examine the performance of the kerosene subsidy as an instrument of redistribution using several measures. I evaluate the subsidy‟s materiality (what budget share does the subsidy represent to households) and its progressivity (do poor households benefit more than the average household). For purposes of comparison with other redistributive policies, I assess efficacy (what share of the total subsidy goes to poor households as intended, not counting indirect benefits32). I explore the distinction between implementation failures and design

31 In transportation, kerosene is used as a cheap fuel substitute to diesel. In construction, kerosene is used in making tar for paving roads. 32 Data on the diversion of kerosene to other markets are unavailable, which makes it difficult to understand who benefits from these diversions. These diversions are often controlled by organized

28 limitations, and evaluate some of the subsidy benefits under ideal implementation conditions. Lastly, I explore the phenomenon of price discrimination by distributors of subsidized kerosene, whereby households pay a wide range of prices for subsidized kerosene. This price variation is an important, and overlooked, component of the benefit incidence for a subset of households.

Overall, this study supports the phase-out of subsidies in rural areas only. However, kerosene subsidies are material and progressive in urban areas. Particularly where access to wood is lacking, kerosene subsidy benefits can be up to 5 to 10 percent of household expenditure, and its removal would more than double these households‟ cooking budgets. However, the goal of policy should be to phase out kerosene demand, rather than the subsidies, by improving access to LPG.

This study relies on data from the National Sample Survey of India for Consumption Expenditure, 2004-05 (NSSO0405) and draws qualitative insights from a primary survey conducted of 450 households in urban and peri-urban parts of Maharashtra in 2009/2010 (See Appendix D).

In Section 2, I describe the various uses for kerosene in different household in Maharashtra, and how these functions influence the subsidy‟s relative benefits. In Section 3, I discuss the measurement approach and the results of the subsidy performance analysis. In Section 4 I discuss the subsidy performance under ideal implementation conditions. In Section 5 I discuss price discrimination by ration shop owners (RSOs). In Section 6, I discuss policy implications.

2 Kerosene Market and Use Characteristics Kerosene use is widespread in India and in Maharashtra in particular. In Maharashtra, seventy percent of households (68 million people) use kerosene. Of these, about 50 million users are in rural areas, and 18 million are in urban areas.

crime syndicates in Maharashtra. See „Maharashtra Cracks Down on Oil Mafia‟, Hindu Business Line, January 28, 2011.

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Kerosene use is not homogenous across the country, particularly between rural and urban areas. Urban use of kerosene is higher in Maharashtra than in the rest of the country, and even more so in the vast slums of metropolitan Mumbai (Figure 3). The average kerosene consumption among households earning less than Rs. 4,000 per month (or $222 PPP) in urban Mumbai is over 10 liters per month. As a result, though 26 percent of kerosene users in Maharashtra are in urban areas, they consume 45 percent of total household kerosene use in the state.

Figure 3: Variation in Urban Household Kerosene Use - Mumbai, Maharashtra and India 2004-05

16 Mumbai Suburbs 14 12 Rest of MH 10 8 India 6 4

Kerosene (Liters/Month) Kerosene 2 0 1 2 3 4 5 6 7 8 9 10 Income Deciles

Kerosene is also used widely across income groups, but rarely as a primary fuel (Figure 4). Rather, kerosene is used more commonly as a backup fuel, to electricity for lighting, and to LPG and biomass for cooking. This is because kerosene is not a preferred fuel. This feature of kerosene is evidenced by the fact that households using multiple fuels rarely, if ever, use kerosene as a primary fuel, either for lighting or cooking.33 The poorest in rural areas – who rely on wood for cooking – and the wealthiest in urban areas – who use LPG exclusively – hardly use kerosene. This is in

33 This was found both in the primary survey and NSSO0405, where households name their primary lighting and cooking fuels.

30 contrast to LPG, which is a preferred cooking fuel, and whose use correlates well with income, as shown in Figure 4.

Figure 4: Cooking Fuel Shares by Income Decile – Maharashtra, 2004-05

(a) Rural

120 100 80

60 LPG

40 Kerosene (MJ per capita) per (MJ

20 Wood Average Cooking Energy Cooking Average 0 1 2 3 4 5 6 7 8 9 10 Income Deciles

(b) Urban

120

100

80

60 LPG

40 Kerosene (MJ per capita) per (MJ Wood

20 Average Cooking Energy Cooking Average 0 1 2 3 4 5 6 7 8 9 10 Income Deciles

Source: National Sample Survey of Consumption Expenditure, 2004-05 Note: Kerosene use below 4 liters is assumed to be for lighting. Two underemphasized characteristics of kerosene use in households are important in understanding the benefit incidence of subsidies: actual kerosene market prices\, and

31 kerosene‟s use as a cooking fuel. These are discussed next, along with how they affect subsidy benefits.

2.1 Kerosene Markets The quantity of subsidized kerosene allocated by government is intended to meet household needs. In reality, households in Maharashtra purchase over 40 percent, and in urban areas over 50 percent, from secondary (black) markets (Table 1).

Households are supposed to purchase all their household kerosene needs through the Public Distribution System (PDS). As part of the PDS, millions of retailers obtain licenses to distribute subsidized kerosene (“PDS kerosene”), among other food rations. Households register their ration needs through ration cards, which indicate various eligibility criteria, on the basis of which each ration shop‟s quota is calculated.

Table 1: Kerosene Consumption by Region and Market: Maharashtra 2004-05

(Monthly Use, Million Liters)

Subsidy Diversions

Quota Actual Black Other Total Use Market Markets

Rural 80 25 12 42

Urban 38 15 16 7

Totals 118 40 28 50

Source: National Sample Survey Consumption Expenditure, 2004-5. Quota: Author‟s „bottom-up‟ calculations of household allocations. Numbers don‟t add due to rounding. For kerosene, unlike other subsidized products, households‟ quotas are defined based on their assumed reliance on kerosene for cooking, and not on their poverty status.34 The quota criteria include two household characteristics: the number of LPG

34 Ration cards are issued to citizens of India, which delineate broad categories of annual income for households: below poverty line (Rs 150,000). Food subsidy quotas are allocated based on these categories.

32 cylinders35, and household size (See Appendix A). Households with two cylinders are not entitled to kerosene. Households with one cylinder are entitled to 4 liters per month. The quota of households without any LPG cylinders increases with family size up to a maximum. Both criteria, however, weakly correlate to income. Lower income households tend to have fewer LPG cylinders and larger families. Thus, in principle, the kerosene subsidy does have the potential to serve as a redistributive instrument, but an imperfect one.

A total of 140 million liters is allocated on a monthly basis to ration shops,36 of which in 2004-05 about 129 million liters on average were claimed for distribution.37 A bottom-up aggregation of household quotas from NSSO0405 indicates that about 120 million liters ought to have been allocated. The discrepancy may reflect a margin of error from using a sample, and the likely mismatch between households actual and recorded eligibility. Regardless, as mentioned earlier, a large share of this allocation is diverted upstream to third parties who sell kerosene to other markets and back to households in the black market. As a result, based on an aggregation of the sample in NSSO0405, only 40 million liters of subsidized kerosene were consumed by households in Maharashtra. Notably, in rural areas the quota is almost double actual consumption. This demand shortfall would encourage kerosene‟s diversion.

While this quantity diversion is well understood, the prices in these markets have not been analyzed. The market prices for kerosene vary widely, but on average are double that of PDS kerosene. These prices are the alternate price most households would face

35 Households purchase LPG in cylinders that typically contain 14.2 kg of LPG. Households pay a deposit to get a cylinder, then subsequently buy refills by swapping out empty cylinders for full ones. Households face delays in receiving refills. Thus, households that can afford two cylinders have a reliable supply. 36 Maharashtra Food, Civil Supplies and Consumer Protection Department. (www.maharashtra.gov.in/english/food/schemesKerosene.php) 37 „State-wise Superior Kerosene Oil (SKO) Uplifted under Public Distribution System (PDS) in India – 2004-5 to 2007-8‟, IndiaStat.com.

33 should kerosene subsidies be lifted.38 More importantly, the subsidy price also varies widely, but only to a minority of households (Figure 5).

Figure 5: PDS Kerosene Prices by Quantity Sold – Maharashtra 2004-05

(a) Subsidy Prices

40 35 30 25 20 15 Highest Intended Subsidy Price

10 Price (Rs/Liter) Price 5 0 0 10 20 30 40 Quantity Purchased (Liters/month)

(b) Black Market Prices

40 35 30 25 20 15

Price (Rs/Liter) Price 10 5 0 -5 5 15 25 35 Quantity Purchased (Liters/month)

Source: National Sample Survey 2004-05. Sales prices shown for all retail sellers in the state.

38 Black market prices vary by region, but are typically set by the prices of fuels that they substitute in other markets, such as diesel in automobiles. Data on black market prices are available in NSSO0405 in household surveys as the price of „other‟ kerosene purchases made outside the PDS.

34

About 5.6 million people in Maharashtra (13 percent of PDS kerosene users) buy PDS kerosene at above Rs 12 per liter (higher than the highest intended price, including transport and profit). In many districts, prices are as much as double the wholesale rate. This pattern was observed in several other states in India, but is only explored in detail for Maharashtra below.

In Section 5, this phenomenon is explored in more detail. The point here is that the subsidy benefits enjoyed by these households differ from others who pay the intended subsidy price.

2.2 Kerosene as Primary Cooking Fuel in Urban Slums A neglected fact revealed in the primary survey is that many households in urban slums rely exclusively on kerosene as a cooking fuel. Datta estimates this group to comprise 10 percent of urban households (Datta 2010). These households on average use more kerosene than all kerosene users, and therefore stand to benefit the most from kerosene subsidies.

In a primary survey of 450 households in urban and peri-urban parts of Maharashtra in 2009/2010, 33 households, all slum dwellers, who did not have access to wood and who stated they could not afford LPG, cooked exclusively with kerosene. These circumstances may be commonplace among the urban poor across India, particularly in large metropolitan areas.

According to the National Sample Survey of 2004-05 (NSS0405) of India, however, a negligible number of households across India, and no households in Maharashtra, list kerosene as their primary cooking fuel. This group may, therefore, be underrepresented in the subsequent analysis.

2.3 Kerosene Use as Secondary Cooking Fuel The central government‟s primary objective in subsidizing kerosene is to give income relief to the rural poor who use kerosene for lighting (Parikh 2010). The policy recommendations for phasing out the kerosene subsidy rests largely on the premise that kerosene is used primarily for lighting in rural areas. The vast majority of rural

35 households do use kerosene primarily, albeit not exclusively, for lighting (See Table 2).

However, kerosene use as a secondary cooking fuel is widespread in India. At least 23 percent of the 7.2 billion liters of kerosene consumed in Indian households in 2004-05 may have been for uses other than lighting.39 In rural areas, this amounts to about 15 percent of total consumption from about 180 million people in 30 million households. In urban areas, 44 percent of kerosene consumption from 53 million people in 11 million households is used for cooking and/or water heating, the latter being largely for bathing.

Table 2: Household Kerosene Use by Function, Region and Priority

Lighting Cooking Primary 12.2m (R) <0.1m (R) 1.3m (U) slumsb (U)

Secondarya 20.0m (R) 17.0m (R) 4.8m (U) 10.0m (U)

Source: National Sample Survey of Consumption Expenditure, 2004-05 Notes: a. Secondary use for lighting calculated as households with electricity and <4 liters/month of kerosene use, for cooking as households with LPG or wood use and >4 liters/month of kerosene for cooking b. Not available: Number of households using kerosene as primary cooking fuel in urban slums

In urban Maharashtra, 10 million out of 17.6 million kerosene users use it as a secondary cooking fuel. As mentioned, Maharashtra‟s household quota allocation scheme is indeed based on household cooking needs. This group of households not only would consume more kerosene than lighting users, but a subset of them might also pay a different replacement price in the absence of subsidies, namely LPG prices.

39 This calculation is based on the assumption that households use no more than 4 liters per month for lighting. Two sources make this a safe upper bound. An average kerosene lantern (~37 lumens) used for 4 hours a day would not consume more than 3 liters per month. In India, lanterns vary from 12-80 lumens. Second, households in all income deciles without electricity access consumed 2.75-3.9 liters per month on average, as per the National Sample Survey of India 2004-05.

36

The distribution across income groups of kerosene use as a cooking fuel also varies between urban and rural areas (Figure 6). These patterns are explained by their relative reliance on LPG, which is more closely tied to kerosene usage. In rural areas, kerosene use increases with income, since LPG use increases with income. Among poorer households, kerosene is used as a backup to wood, albeit to a lesser extent, when wood is unavailable (such as during monsoon), or too expensive (where purchased).

Figure 6: Kerosene Use for Cooking/Water Heating – Maharashtra 2004-05

350,000 Rural 300,000 Urban

250,000

200,000

150,000 Population

100,000

50,000

0 Income Centiles

Note: Cooking use assessed as household use above 4 liters per month. Source: National Sample Survey of Consumption Expenditure, 2004-05

In contrast, in urban areas, such secondary use is highest among lower and middle income groups, but then decreases with income at the highest income levels, as more households have two LPG cylinders or piped gas supply.

2.3.1 Drivers of Kerosene Use as a Secondary Cooking Fuel An important feature of kerosene‟s secondary use is that it is driven not only by unreliable supply of households‟ primary fuel, but also by the desire to save fuel costs. Households for which kerosene is cheaper (“Economy Users”) use kerosene regardless of LPG availability.40 Based on a heuristic to identify Economy Users (discussed in Section 3), there are about 4 million such users in Maharashtra. One indication of this phenomenon is that a large number of households use different fuels for cooking and

40 A number of other factors influence households‟ cooking fuel choices, other than fuel economy otherwise „Economy Users‟ would not use LPG at all. This is a rich and underexplored topic in literature that merits further research.

37 for water heating (which is for bathing), regardless of their supply conditions.41 For example, in the primary survey 60 percent of all households, and almost all low- income households, use different fuels for cooking and water heating respectively, regardless of their primary fuel. Most urban households, particularly in Mumbai, use LPG for cooking and kerosene for water heating.42

The importance of the distinction between Economy Users and those using kerosene due to insufficient LPG supply (“Last Resort Users”) is that the former benefit less from subsidies, since they can switch back to LPG if cooking with kerosene becomes more expensive.43

It would seem that the margin of subsidy benefits for Economy Users is small. For these households, the economics of cooking with LPG and kerosene depend on their relative energy (rather than life cycle) costs, since these households already own an LPG range and kerosene stove (Figure 7). A rough calculation of the energy used for heating bath water shows that the total savings from using subsidized kerosene in comparison to subsidized LPG may be up to Rs 30 per month for a family of seven. 44 This is less than a half a percent of poor households‟ monthly expenditure. Such households may be mistaken about relative cooking costs, or consider these savings worthwhile.

Households that cook with black-market kerosene pay approximately the same fuel price as LPG (about Rs 22/kg on average in urban Maharashtra), which implies they pay more on an energy basis to cook with LPG. This suggests that LPG users who buy black market kerosene for cooking must also be Last Resort Users who lack reliable access to LPG.

41 This was observed in the household survey. 42 A small number of households who purchase commercial wood may also similarly use kerosene only due to the subsidy. Due to the absence of reliable wood price data, this group cannot be readily identified. 43 The heat content of the fuels and their stoves‟ efficiencies determine their cost in energy terms, given fuel prices. 44 This estimate is based on each family member bathing every day, each using 10 liters of hot water heated from 10 degrees C to 70 degrees C, and with savings of Rs 0.25 per megajoule of delivered energy from using kerosene instead of LPG.

38

Figure 7: Kerosene vs. LPG Delivered Fuel Cost Comparison (2004-05 prices)

2.5

2 Last Resort Kerosene Users 1.5 Economy

1 Kerosene Users (Rs/MJ) LPG 0.5

Delivered Energy Cost Cost Energy Delivered Kerosene 0 0 10 20 30 40 Fuel Cost (Rs/kg) Assumptions: Stove efficiency of 40% and 54%and fuel content of 43.1 and 45.8 MJ/kg for kerosene and LPG respectively

In summary, Economy Users may represent a minority of kerosene users. However, the financial risk to them of removing subsidies is significantly less than others, given the thin margins of savings from using subsidized kerosene over LPG. To the extent possible, differentiating these households would be important in calculating the distribution of subsidy benefits.

3 Assessment of Subsidy Performance In this section, I discuss the merits of kerosene subsidies as a redistributive policy based on three measures: materiality; progressivity; and efficacy. I first present the measurement approach and metrics. I then discuss the results and the drivers of low efficacy.

3.1 Measurement approach I first present the method for calculating individual household benefits, in terms of budget share, based on the categorization of kerosene users discussed in the previous section. Based on these budget shares, I estimate progressivity and efficacy.

39

3.1.1 Materiality In contrast to prior studies that focus on mean benefit incidence for households in an income decile, I calculate the benefit for different types of households based on fuel choices, with the goal of providing insights for targeting future policies.

The value to households of the subsidy is a reduction in fuel expenditure, which enables more discretionary spending on leisure. The simplest way to assess this value would be to assume households have inelastic demand for kerosene, and estimate savings as consumption multiplied by the difference between the subsidy price and black-market prices. However, this would ignore the fact that many households are elastic to price changes. In particular, Economy Users would switch to LPG, while Last Resort would be forced to continue using kerosene and pay black market prices.

Identifying Economy Users required a heuristic in the absence of data on LPG reliability. Note that all households fall into the first category (including primary cooking/lighting users), except for those select LPG users whose cost of using subsidized kerosene is less than that of LPG in energy terms. The prevailing relative prices of LPG and kerosene for a household were used as proxies to identify this group. If households used kerosene as a supplemental fuel even when LPG was cheaper on an energy basis, they presumably had fuel availability issues. They are thus assumed to be Last Resort Users. If the LPG price falls between the subsidized and black market price of kerosene, households are assumed to be Economy Users.

The real income loss I caused by the subsidy removal can be estimated as follows for a given percentage change in the kerosene price,  pk for the two groups:

Last Resort Users (No Fuel Switch):

I  Qk  p k  Q k1   p k  k  p k (1   p k ) (1)

Economy Users (Switch to LPG):

I  Qk  p k  Q LPG 1   p k  klPG  p LPG (2)

40

Where k is the own-price elasticity for kerosene demand; kLPG is the cross-price elasticity of LPG to a change in kerosene price; Qk and pk are the original quantity and price of kerosene, and QLPG and pLPG are the original quantity and price of LPG (all in energy units). Note that kerosene demand is inelastic for last resort users, since cooking is an essential function. However, to allow for some conservation a sensitivity analysis incorporates elasticity.

3.1.2 Subsidy progressivity Progressivity is an important metric for policies that have a wide impact. A poverty index, for example, would not provide an indication of the relative benefits between the poor and non-poor. Thus, a policy that reduces the poverty gap can be regressive if it benefits middle income groups to a greater extent. Such a policy could be an expensive instrument of redistribution.

I adapt a measure of „pro-poor‟ growth used in the development economics literature, which uses a „Growth Incidence Curve‟ (GIC) to measure the extent to which income growth accrues to the poor relative to what accrues to those above poverty, as defined by some poverty threshold (Grosse, Harttgen et al. 2008).

Specifically, a variation of the GIC is constructed with the average income percentage change for every population centile in order of increasing income. If the slope of this curve is increasing (decreasing), the subsidy provides greater (lesser) benefits to higher income groups on an individual household basis. In aggregate, the average percentage change in income for all centiles (H) below the threshold (μp) is compared to the average percentage change (μ) for the entire population (n). The former would be higher for a progressive policy.

1 H  p II i/ i (3) H i1

1   IIii/ (4) n n

p  Progressive subsidy

41

3.1.3 Subsidy Efficacy One way to compare redistributive policies is to consider them as various mechanisms for enabling a lump sum transfer to particular household groups. In this vein, the efficacy would be the share of the total subsidy value that the intended beneficiaries receive. The inverse of efficacy can be interpreted as the cost per unit of income relief provided to the intended beneficiaries. The efficacy can be calculated as follows:

 I HH (5) CPQ Subsidy. AggQuota

The denominator represents the aggregate subsidy value, or fiscal cost, where QAggQuota is the total quota allocated to targeted households. C is the cost of production, and

Psubsidy is the subsidy price. The numerator represents the actual savings to households, where ∆I is shown in Equations (1) and (2) in Section 3.1.

I evaluate the metric for two sets of beneficiaries: all households, and only those households earning below $2/day. The former captures the subsidy value that is lost due to reasons other than targeting among household groups, while the latter includes targeting failures as well.

3.2 Data All household expenditure and household fuel use data are drawn from the NSS0405. Surveys show quantities and prices for all fuels, including both subsidized and black market kerosene. No estimates are available for kerosene cooking elasticity. There are only two known studies that estimate own- and cross-price elasticities of household fuels in India (Gupta and Köhlin 2006; Gundimeda and Köhlin 2008). However, these estimates are not appropriate for several reasons.45 I use a range of own-price elasticities of -0.1 to -0.25, with the former in the base case. For similar reasons, the base case cross-price elasticity for LPG was assumed to be 0.9. But for some

45 The studies use cross-sectional data (and therefore estimate long-term elasticity) for the entire country and do not differentiate cooking from lighting.

42 conservation in the face of higher prices, Economy Users would have no reason to shift less than the full amount of energy to their LPG stove.

3.3 Subsidy Performance Results Here, I present the results of the analysis.

3.3.1 Materiality Among those spending less than $2/day across all Maharashtra, the average income relief from the kerosene subsidy amounts to 0.3-0.5 percent of household expenditure in rural and urban areas respectively. However, the benefits vary widely, from 0-1.3 percent of household expenditure. Of the rural and urban kerosene-using population, almost 24 percent and 47 percent respectively receives benefits that exceed 1 percent of total expenditure (Figure 8). With an elasticity of -0.25 (vs. -0.1 in the baseline), these figures drop to 10 and 33 percent. Savings are highest in metropolitan urban areas, such as in Mumbai, and in remote districts with limited LPG supply.

Figure 8: Population Share by Kerosene Subsidy Benefit – Maharashtra 2004-05

60% 50% Rural 40% 30% 20%

PopulationShare 10% 0% >1% .5%-1% .25-.5% 0-.25% Subsidy Benefit (Share of Household Income)

Note: based on estimates of „maximal‟ savings – kerosene price elasticity of -0.1. Total Population: Urban – 37 million; Rural – 55 million. For the poor in District 1 – a remote district with 1.3 million people - the savings on average are 1.7 percent of their monthly expenses, and over 5 percent for those earning less than $1/day. Considering that the poorest urban households spend 10

43 percent of their income on electricity, this is a substantial amount to spend on non- food essentials.46

Among LPG users, Last Resort users have an average savings of 0.5 percent, while Economy Users have savings of 0.4 percent, with the reduction being higher at upper middle income levels.

This finding has important implications for the relationship between LPG and kerosene policies. The more liquid and reliable the LPG market, the less upper income households would use kerosene. This would improve the progressivity of kerosene subsidies, simply by altering market incentives. Note that this finding is different from the conventional wisdom that LPG pricing alone would influence kerosene use.

3.3.2 Progressivity Figure 9 below shows the cumulative average savings as a share of household expenditure. Several observations stem from the figures. First, in rural areas, the subsidy benefits are regressive. The cumulative average savings rate (Equations (3) and (4)) is 0.37% for those who earn under $1/day, and 0.41% for those who earn below $2/day.

Figure 9: Kerosene Subsidy Progressivity: Urban and Rural Maharashtra 2004-5

0.7% 0.6% Urban - Progressive 0.5% 0.4% 0.3% Rural - Regressive 0.2%

0.1% (MonthlyExpense Share Cumulative Mean Savings MeanSavings Cumulative 0.0% Income Centiles

46 Rao (2010) „Distributional Impacts of Climate Change Mitigation: Case Study of Electricity in Maharashtra

44

However, in urban areas, the subsidy is consistently progressive for the entire population, using the same thresholds.47 The average income relief is 0.58% for lower income groups, and 0.53% for middle income groups.

To emphasize the importance of regional differences in kerosene use and benefits, Figure 10 shows the extreme subsidy progressivity in District 1 stemming from the dependence of the urban poor on kerosene for cooking.

Figure 10: Kerosene Subsidy Progressivity – Nandurbar District, 2004-05

0.04 0.035 0.03 0.025 0.02 0.015

0.01 (MonthlyExpense Share Cumulative Mean Cumulative Savings 0.005 0 Income Centiles (of Maharashtra)

3.3.3 Policy Efficacy The efficacy of the kerosene subsidy (Equation (5)) from the perspective of all households is at best ~26.5 percent. That is, for every 100 Rupees of subsidy, only Rs 26.5 of income relief is delivered to households directly. The efficacy for delivering income relief to those earning under $2/day is 17 percent. Thus, for every rupee of income transferred to these poor, six rupees has to be spent by the government.

The low efficacy reflects the fact that only 31.3 percent of the kerosene picked up by wholesalers was delivered to households through the PDS in 2004-05, based on NSSO0405. The difference of ~ 5 percentage points, between the quantity (31.3) and benefit (26.5) shortfalls, is attributable to price discrimination. That is, households pay

47 Note that prices in urban areas are significantly higher, so the same cutoff reflects greater poverty than in rural areas. Data were unavailable to create price-adjusted poverty thresholds.

45 prices that include actual transport costs and rents in addition to the wholesale subsidy price.

4 Kerosene Subsidies and Ideal Implementation The shortfall in the subsidy value that reaches households can be attributed to design failures and implementation failures. For policy reformists, this distinction would be important. Even under ideal implementation conditions, a kerosene subsidy can only benefit kerosene users up to the use of their quota. The mismatch between demand and the quota reflects the limits of the value of the subsidy even under ideal implementation conditions (Figure 11). The correlation between household quotas and their usage was found to be only 22 percent in rural areas, and 58 percent in urban areas, indicating that the quota design is better suited to urban kerosene needs. Implementation failures, on the other hand, are reflected in the fact that households purchase part of their entitled kerosene quotas in the black market, or that they fail to obtain the intended subsidy price for the part of the quota they do obtain. Indeed, 6.7 million of the 9.7 million urban kerosene users in Maharashtra purchase at least as much kerosene in the black market as the shortfall in their quota. Both these types of failures and their consequences for efficacy are discussed next.

Figure 11: Kerosene Subsidy Quotas and Actual Use

(a) Urban

12 10 Black Mkt 8 PDS 6 Quota

4 (Liters/Month) Household Use Use Household 2 0 1 2 3 4 5 6 7 8 9 10

46

(b) Rural

10 8 Diversion to 6 Other

4 Markets (Liters/Month) Household Use Use Household 2 0 1 2 3 4 5 6 7 8 9 10

Income Deciles 4.1 Design Failures Most poor households do not claim their entitled share of subsidized kerosene, as reflected in the gap between the average quota and total usage (Figure 11). This is the case in rural areas across all income groups. As mentioned earlier, most rural households cook with wood, since wood is cheaper than kerosene and preferred. Since the government allocates quotas based on cooking needs, but rural households purchase PDS kerosene mostly for lighting, rural households forego most of their quota. In urban households, on the other hand, kerosene‟s use for cooking aligns with the quota, except for the lowest income households, many of whom have access to wood, with the exception of those in metropolitan areas, as discussed earlier.

At the same time, about a fifth of households in the lowest three deciles in urban areas buy kerosene in excess of their quotas (Figure 12). This heterogeneity among the urban poor stems from varying access to wood, as reflected in the fact that those who forego their quotas consume on average about four times the quantity of wood as those that consumer above their quotas. In rural areas, because kerosene use increases with income, the share of households whose kerosene purchases exceed their quota also increases with income.

47

Figure 12: Households with Insufficient Kerosene Quotas – Maharashtra 2004-05

30.0% Rural 25.0% Urban 20.0% 15.0% 10.0% 5.0% 0.0%

Share of Households in Decilein Households of Share 1 2 3 4 5 6 7 8 9 10 Income deciles

Note: Data show households that purchase any amount of kerosene in excess of their quotas.

Thus, the discrepancy between allocated quotas and demand reinforces the difference in kerosene benefits between urban and rural areas. The progressivity of subsidies would likely increase in urban areas from better targeted, and potentially higher, quotas for kerosene subsidies. In rural areas, on the other hand, the extent of unused quotas only provides incentives for their diversion to other sectors.

The question then arises as to what the best attainable efficacy would be under „ideal‟ implementation conditions, given the design limitations. This is discussed next.

4.2 Subsidy Performance under ‘Ideal’ Implementation Subsidy benefits in ideal implementation conditions entail that households satisfy all their kerosene requirements through the PDS and at prices corresponding to the intended subsidy price (including a legitimate distance-sensitive transportation charge). The three metrics – materiality, progressivity and efficacy – under „ideal‟ implementation conditions are as follows.

If ideally implemented, the material impact of the subsidy would be double that of actual implementation. For urban households earning under $2/day, the benefits would amount to 1.1 percent (compared to 0.5 percent in practice).

48

What subsidized kerosene does reach households does not appear to have a distributional bias compared to their ideal delivery, implying that income does not seem to be a basis for denying households their quota.48

With regard to efficacy, the share of subsidy value that would go to households would increase to 39-46 percent, using a subsidy price (including transportation) of Rs 11 and 10 per liter respectively.49

4.3 Summary In urban areas, coverage of the poor is relatively low, but the materiality of subsidies is higher, and black market purchases represent a higher share of total consumption. This makes the subsidies progressive, and their removal potentially costly for particular urban groups that have few alternative cooking fuels. For these households, income relief from kerosene subsidies amounts to a range of 1-5 percent of their monthly expenditure.

In rural areas, the subsidy coverage is high, but of low material value and regressive, since it caters to LPG users, who are mostly high-income households. The bulk of households that use kerosene for lighting obtain income relief of 0 to 0.4 percent. Most of the quota goes unused.

Most of the loss in subsidy value seems to result from poor suitability of kerosene subsidies as instruments of redistribution. Even under perfect delivery of subsidies to households, the policy efficacy improves from 26 percent to 46 percent at best.

5 Ration Shop Owners – Profit-Maximizers, Not Public Servants This section explores the hypothesis that ration shop owners (RSOs) are profit- maximizing monopolists, who price-discriminate among kerosene consumers on the

48 In the primary survey, several interviewees described methods of discrimination by ration shop owners that could be influenced by their income (such as conditioning the sale of subsidized kerosene on other grocery purchases, political and social connections, etc). But these did not manifest in NSSO data as robust trends across the state. Their examination would be a topic for future research. 49 Using a distance-sensitive transportation surcharge for households by district would yield a figure in between these.

49 basis of the only household characteristic observable by them: the quantity of consumption.

The regulated wholesale price of kerosene in Maharashtra is Rs ~9.20 per liter. In each district, a district collector determines the PDS kerosene retail price by adding a profit and a transportation surcharge, each of which is supposed to be less than Rs 0.50 and Rs 2 per liter respectively. The transport charge increases with a district‟s distance from Mumbai, the hub of kerosene wholesale supply.50 In interviews it was learned, however, that these surcharges are often insufficient in many locations to even recover actual transportation costs.

Individual ration shops are also monopolists with a fixed set of customers. Households are assigned to specific ration shops to collect their entitlement. Thus, RSOs have both the incentive and opportunity to maximize their profits by charging customers above marginal cost.

There appears to be a pattern that prices of subsidized kerosene are marked up in inverse proportion to the quantity sold (Figure 13).51

Figure 13: Subsidy Price by Purchased Quantity – Maharashtra 2004-05

14 12 10 8

6 Rural (Rs/Liter) 4 Urban 2

KerosenePrice Subsidy 0 0 1 2 3 4 5 5-10 10-20

Purchased Quantity (Liters/month)

50 The Secretary of the Food and Civil Supplies Department in the Maharashtra government indicated that the wholesale PDS kerosene price was Rs 9.18 per liter in 2007, and that with transportation charges should not exceed Rs 10.85 per liter (Interview on February 3, 2010). 51 In a few cases, RSOs apparently charge below their marginal cost, which seems inexplicable. While some of these are likely to be data errors, in the primary survey it was revealed that RSOs often arrange bargains with customers to purchase other commodities in exchange for a price discount on PDS kerosene.

50

This pattern is not discernable for market kerosene, where average prices and standard deviation do not vary significantly with quantity. One explanation for this pattern may be that transport charges happen to be higher in regions where average quantities purchased are lower (Figure 14), and that RSOs include their actual transport charges. Alternatively, households may report market kerosene purchases as PDS kerosene in NSSO0405. However, this pattern is observed for only smaller quantities, and is consistent across many states. This makes the latter a less plausible explanation than the former.

Figure 14: PDS Kerosene Prices, Quantities, and Transport Distances by District

14 1200 Average Prices 12 1000 10 800 8 600 6 (km) 400 4

Average Price Average (Rs/liter) 2 200 Average Distance Average fromMumbai 0 0 0 5 10 15 Average Purchased Quantity (liters/month) Trend line shows the best fit linear relationship

This relationship between PDS price and quantity was tested quantitatively. The following hypothetical price model was tested using regression analysis on NSSO0405 data.

Pki = MC + tk + bln(qki) (6)

Where, for a household i served by a ration shop in the urban or rural portion of district „k‟:

Pki: PDS kerosene price MC: regulated „wholesale‟ price of PDS kerosene tk: transport commission qki: PDS kerosene demand

51

The hypothesis of quantity-based price discrimination is based on two premises: (a) that kerosene demand is the most reliable observable indicator of household‟s kerosene budget52; and (b) households‟ kerosene price elasticities are proportional to their kerosene budgets. With all else held equal, households with a higher budget face a larger income effect from a given price change, to which they are therefore more elastic.

Two econometric models were used to test the hypothesis. PDS kerosene prices (pi) 53 were regressed against the log of purchased quantity (qi’) and distance (tk) from Mumbai as a proxy for transport commission (Model 1). The transport coefficient accounts for the actual transport charges, which should be proportional to distance. In

Model 2, fixed effects for districts (Dk) are used instead of distance, due to multicollinearity, and also to account for discretionary behavior of the district collectors who finalize the PDS kerosene price.54 This variable would capture an alternative source of rent extraction (by the district collectors) that could explain pricing above regulated rates.

Econometric model:

Model 1: pi = β0 + β1tk + β2qi’ + εi

Model 2: pi = β0 + Dk + β2qi’ + εi

Where, in relation to Equation (6):

β0 = MC + D0 (in Model 2); β1 = b; and qi’ = ln(qi)

The results of the regression analysis are shown in Table 3 for the two models.

52 Though RSOs see households‟ income level and LPG ownership on ration cards, this information is unreliable (households that do not show LPG ownership may well have them), and does not show LPG supply reliability. 53 Similar results were obtained with a model using the inverse of quantity (1/q), instead of the logarithm. 54 Data were not available on ration shop customers to include fixed effects for individual ration shops.

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Table 3: PDS Kerosene Price Discrimination Model Results

PDS Price Model 1 Model 2

Constant 11.22 13.73 (.09) (0.24)

Log(PDS Quantity) -.68 -0.15 (0.04) (0.04)

Distance (00‟s km) .16 - (0.01)

District Fixed Effects - -0.78 to 3.6 (relative to District 1) (0.32, 0.25)

R-squared 0.24 0.60

Observations 3,909 3,909

Implied Population 8.5 mil 8.5 mil The analysis indicates that kerosene quantity and distance are both statistically significant predictors of the PDS price paid by households. According to Model 1, a difference in purchased quantity of 10 liters a month implies a price difference of about Rs 0.70 per liter, and for every 100 km away from Mumbai, the price would increase by Rs 0.16 due to transport charges. The extrapolated maximum transport charges (~Rs 1.60) match that indicated by the government (See footnote 50). However, this model has modest explanatory power (R2 = 0.24). Presumably this is because it omits the discretionary powers of the district collector and RSOs.

Model 2 accounts for this discretion with district fixed effects, but without transport separated. Predictably, this adds significant explanatory power (Model 2, R2 =.60). But the coefficient for ln(q) falls significantly. However, this coefficient may be underestimated, because the fixed effects are also correlated with the main independent variable, quantity. That is, the average quantities consumed by households vary by district. Moreover, the average prices in districts also vary in inverse proportion to the average quantity consumed, as shown earlier in Figure 14. Nevertheless, these results are of interest here, because despite the unreliability of the coefficients, F-tests confirms the statistical value of all explanatory variables in the model.

53

These results are suggestive of the presence of quantity-based price discrimination by RSOs, and ambiguous about the extent of their impact. It is possible that the district collectors are responsible for the bulk of the price markup, rather than the RSOs. Further, several related questions merit further investigation. Do RSOs mark up prices based on prevailing black market prices? Why don‟t RSOs mark up prices to households that purchase large quantities, when similar households seem to pay higher prices in the black market? It is likely that RSOs‟ exercise some caution to avoid scrutiny that may affect this behavior.

Despite these unanswered questions, the analysis provides sufficient evidence to reject the null hypothesis that quantity-based price discrimination does not play a role. While price discrimination affects only a minority of users, combined with the clear incentives for such behavior and limited accountability of RSOs, its occurrence bring into question the prudence of continuing with RSOs as agents of public service.

6 Conclusions and Policy Implications The government‟s inclination to phase out kerosene subsidies is premature. Reforming the kerosene subsidy requires different approaches in the short and long term. Current policy analysis assumes away important distributional benefits of the subsidy in urban areas, where access to biomass and affordable LPG is limited. On average, income benefits of the kerosene subsidy are about 0.5 percent of household expenditure, but among the urban poor, which include over 2 million users, income shocks of 1-5 percent are likely, corresponding to a doubling of households‟ cooking budgets.

In the short term, the efficacy of the subsidy can be improved without losing these limited benefits by redesigning households‟ allocations of subsidized kerosene to better reflect households‟ needs – cooking in urban areas and lighting in rural areas. This would reduce the total subsidy requirement considerably, thereby preventing the loss of about half of the 74 percent of the subsidy value that gets diverted as rents along the supply chain.

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In the long term, whether the subsidy distribution system should be reformed or eliminated requires a broader evaluation of alternative redistributive instruments. This study shows that kerosene is not a preferred fuel, rather its use and the value of the subsidy are tied to the availability of other preferred fuels, such as LPG and wood. This emphasizes the importance of the kerosene subsidy as only an instrument of redistribution. Alternative redistributive instruments should, therefore, be evaluated among other things based on the metrics used in this study. The feasibility and cost of institutional reform is also important to study in evaluating alternative redistributive policies. The majority of the loss of subsidy value results from rent extraction at numerous points of distribution that are controlled by entrenched interests. These entrenched interests that drive the kerosene black market may make subsidy reduction or removal politically challenging.

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CHAPTER 3 – DISTRIBUTIONAL IMPACTS OF CLIMATE CHANGE MITIGATION IN INDIAN ELECTRICITY: CASE STUDY OF MAHARASHTRA

1 Introduction With 17 percent of the world‟s population, India‟s participation in climate mitigation is imperative if the world is to stabilize GHG emissions at safe levels. Achieving a safe target level will require a reduction in emissions growth of up to 30 percent below business as usual (BAU) in developing (non Annex I) countries by 2020 (den Elzen and Höhne 2008). While India is the fifth largest emitter, it is ranked 149th in terms of per capita emissions.55 It is ironic that while the Indian government firmly rejects adopting climate mitigation commitments, almost half of electric capacity additions in last decade have come from low carbon sources (Rao 2009). The government pledged to reduce carbon intensity of GDP by 20-25 percent below 2005 levels by 2020 at Copenhagen. If India continues to invest in low-carbon electric capacity, as a result of international climate policy or energy security, electricity prices will likely increase.

With a third of the world‟s poor in India,56 the distributive impacts across households of higher electricity prices are an important equity and political consideration. Due to the absence of a broad income tax base (Piketty 2009), policymakers rely on price- based subsidies as instruments of redistribution. This paper explores the efficacy and trade-offs of using electricity policy to avoid adverse distributional consequences of climate change mitigation.

The limited literature on climate mitigation impacts in India focuses on aggregate metrics, such as slowed GDP growth (Mathy and Guivarch; Intergovernmental Panel on Climate Change 2007; Parikh 2009) or on emissions reduction potential (Murthy, Panda et al. 1997; Khanna and Zilberman 2001; Reddy and Balachandra 2006). Household-level economic impacts, however, depends on several factors, including

55 Climate Analysis Indicators Tool, World Resources Institute. 56 World Bank, India.

56 income distribution, patterns of energy consumption, and pricing structures. Some studies have examined the impacts of price increases to households of direct energy consumption (Hope 1995). However, indirect electricity costs are not considered.57 In India indirect electricity costs may be significant because industrial electricity prices in India are among the highest in the world (Nagayama 2007; Rao 2009).

In this study, I assess the consumption-side distributional impacts of alternate pricing policies to recover low carbon investments in electric capacity in the state of Maharashtra, India. I use a partial equilibrium analysis of the electricity sector, consisting of a household demand model, and a regulatory pricing model. An input- output (I/O) analysis propagates industrial electricity price changes to household consumption expenditure. Alternate pricing policies are selected to maximize different social welfare objectives and to meet sectoral supply and revenue constraints. I evaluate price impacts under two scenarios for electric capacity portfolios, representing the carbon intensity (of energy) of the BAU fuel mix, and a 20 percent reduction. The latter would be equivalent to meeting India‟s pledge in the Copenhagen Accord while keeping energy intensity fixed.

This study uniquely models actual institutional conditions in Maharashtra‟s electricity sector. In particular, households‟ average prices under block tier pricing and their unequal supply interruptions under chronic scarcity conditions are modeled. With interruptions ranging from 0-10 hours per day, households‟ vulnerability to price increases depends on their actual supply. New data on electricity service conditions have been collected through a primary survey. The use of input-output analysis to capture indirect energy price impacts is new in the Indian context. Previous studies have used I/O to capture carbon footprints (Murthy, Panda et al. 1997; Parikh, Panda et al. 2009) or energy intensities of households (Pachauri and Spreng 2002).

Section 2 outlines the simulation model. Section 3 describes the data sources, including the primary survey. Section 4 outlines the scenarios of social welfare and

57 Indirect costs refer to the price increases of household consumption goods other than electricity as a result of increased production costs, which in turn result from the increase in industrial electricity prices.

57 pricing. Section 5 presents the results, including the indirect price effects, the optimal pricing strategies and their distributive impacts on households, including sensitivity analyses on industrial price elasticity, and households‟ decisions to invest in energy efficiency. Section 6 summarizes the key policy implications.

2 Model Description Households and a regulator are the main actors in the model (Figure 15). Households derive utility (welfare) by consuming goods and services, including electricity service. Utility is represented using a constant elasticity of substitution (CES) with two goods, direct electricity, E, and a composite bundle of other goods, X, (whose production requires electricity consumption). Households allocate expenditure on a monthly basis (the billing cycle for electricity) to E and X. In response to changes in residential and industrial electricity prices, households can shift consumption between the two based on their relative prices and households‟ elasticities of demand in order to minimize (maximize) welfare losses (gains).

Figure 15: Electricity and Welfare Model Simulation Approach

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Regulators set electricity prices on an annual basis with the objective of maximizing household welfare based on different social and political objectives. Regulators choose residential and industrial rates, subject to supply and revenue constraints.58 The supply constraint is that in any month supply cannot exceed demand.59 If demand exceeds supply, excess demand is shed, with the outages being distributed among households according to an exogenous rationing scheme, Q. Under the second constraint, utilities should earn revenues only enough to recover costs and earn a fixed rate of return on an annual basis. Regulators influence the distribution of welfare across households based on how they vary prices, and how much this choice causes demand to be rationed.

2.1. Household Decision Model Households choose between ES, electricity service, and X, a composite bundle of other goods and services based on their relative prices, pe and px, and their total expenditure, I (which is a proxy for income). The utility they obtain is characterized by the CES utility function.

With (1)

s.t.

peE + pxX = I

σ = Elasticity of substitution between E and X α = Value share of X, season-dependent (summer and winter)

Households‟ electricity budget, θ, is related to σ and α as follows:

E 1  0 where C = (2)

IC0 1

58 The production, distribution and grid operation functions of the utility are subsumed into these supply and revenue constraints. 59 In grid operation, this constraint has to hold at all time scales. For the purposes of revenue accounting, monthly representation suffices.

59

This relationship is used to calibrate α for households in the baseline scenario using the electricity budgets obtained from field data. Note that households actually get value from electricity service, for which consumers choose both their levels of electricity and the appliances that deliver this service. In the short term, households choose the utilization rate of an appliance stock that delivers a set of services, in response to price changes. In the long term, households may invest in energy efficient appliances to reduce their welfare losses if they expect price increases to sustain. Here, I focus on a short-term analysis. Henceforth I make no distinction between ES and E. In Appendix C, I describe in more detail the assumptions underlying short-term behavior, and describe an enhanced model that includes investments in energy efficient appliances as an additional choice variable. The results of this long-term decision model are discussed as a sensitivity in the Results section.

Taking First Order Conditions, this gives the following optimal consumption levels for E and X, for incremental changes in their prices:

(3)

Where a =

Note, assuming px constant at 1, this yields an elasticity of household demand to residential electricity price (λe) of:

λe = -

Household welfare under a new set of prices ( pe, px, ) is calculated as deviations from the baseline. Utility is cardinalized as an equivalent variation of income, V, against the baseline (pe0, px0), given by the minimum expenditure required at baseline price levels to achieve the utility associated with the new prices:

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minV ( pex00 E p X ) EX, stUEX.. (,) UEpp ((, ),(, Xpp )) e x e x (4)

Noting that pe0, px0 = 1 in the baseline, and that the RHS is a constant.

With outages, households are forced to consume at suboptimal levels, leading to a lower welfare level, V . This is given by the same calculation in (4), but substituting E for E, from (7). This can be interpreted as the minimum expenditure required at baseline prices to achieve the utility obtained by consuming the rationed level of demand at the new price levels.

2.2. Regulator Decision Model L M H The regulator sets prices pg , pg , pg and pind on an annual basis to satisfy a number of political and social objectives, but he has to ensure that utilities recover costs and earn a fixed rate of return. In practice, the rate-setting process is relatively ad-hoc, and difficult to ascertain, let alone simulate. The Electricity Act, 2003 mandates that regulators reduce cross subsidies so that all customers pay at least the average cost power. But because electricity policy is an instrument of social policy, regulators set prices based on their official mandate along with the interests of different political constituencies.

Thus, I evaluate scenarios of plausible, but hypothetical, social welfare functions (SWF) to represent different political objectives, and incorporate the regulatory mandate as constraints in particular scenarios. I evaluate two SWFs: (a) a utilitarian function, to maximize economic efficiency; and (b) a poverty-inequality index (“equity” function), to measure income distribution below a poverty threshold.

2.1.1 Economic Efficiency All households‟ consumption counts equally towards social welfare. The social objective is to maximize aggregate welfare, the sum of all households‟ welfare

61 changes from a given pricing policy.60 The policy motivation of this objective is in part to encourage growth. The greater the aggregate welfare increase, the higher the potential for savings and investment in future growth.

a (5)

Vim, is the indirect utility in season m for household in income group „i‟(Equation 4) wi is the number of people in income group „i‟

2.1.2 Equity The purpose of this scenario is to minimize welfare losses to low income consumers. Regulators in fact show concern for the welfare of the poor, as reflected by the fact that Maharashtra has a lifeline rate. I use a poverty-sensitive inequality index developed by Amartya Sen (Sen 2008) that focuses on income distribution below a poverty threshold. Only income changes below the threshold change the index. Income increases (decreases) below the threshold cause the index to decrease (increase), and with increasing weight the lower the income of the group. Note that this metric is deliberately chosen to represent an extreme scenario of diminishing marginal utility of income, as compared to other conventional formulations, such as the logarithm of income.

n (6)

H is the poverty headcount (number of people below the poverty threshold) I is the poverty gap (difference between the threshold and the average below- poverty income, expressed as a percentage of the poverty threshold income)

Gbp is the Gini coefficient below the poverty threshold

I use a threshold of $2/day, which includes the lowest two income groups.

60 Note that producer surplus in the electric sector is invariant across pricing scenarios, because utilities recover costs and earn a regulated rate of return in all cases.

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Regulators may face a trade-off between economic efficiency and equity in how they design a price structure to recover higher supply costs. This is not predictable a priori, due to the absence of a closed form solution to the regulator‟s pricing problem. In standard utility pricing, where regulators have to price above marginal cost to meet a zero-profit revenue constraint, price discrimination in inverse proportion to elasticity is a common approach to maximize efficiency(Shepherd 1992). In this context, low income households are more elastic to direct electricity prices, and less elastic to indirect prices, relative to high income households. On net, for a proportional change in all rates, welfare changes are proportional to aggregate expenditure, which tends to favor higher income households, and therefore flat pricing. However, regulators‟ capacity to price discriminate is limited, as is the revenue recovery potential from individual income groups.

2.1.3 Regulatory Constraints Two constraints, by mandate, enter into all scenarios – (a) matching supply and demand, and (b) recovering costs within a margin of error.

for each season, m.

≤

m2 m2 H H pEg i, m   pEind indirect,m mi1 m1

Where:

and are the implied final levels of consumption by each household in the respective tiers of the tariff structure ( + = i,m).

FCG and MCG are fixed and marginal costs of generator type G.

YGm is the unit output in season m of G (modeled as conglomerate units for each fuel type)

TDHT and TDLT are Transmission and Distribution loss percentages, which includes revenue losses from both technical energy losses and theft, for high and low voltage levels.

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2.2 Modeling Supply Rationing Since supply chronically falls below demand, balancing supply and demand requires that utilities‟ ration scarce supply. However, there are many ways to distribute scarcity. The utility delineates and prioritizes regions based on their profitability.61 For a given supply-demand deficit, utilities interrupt power to households for equal hours within regions, and for fixed proportions of hours across rationing priority regions. Households‟ location determines their hours of interruption. Households in the profitable city of Mumbai experience virtually no outages, while households in agriculture-dominated rural areas have 8-10 hour daily interruptions.

I model the utility‟s actual rationing schedule as a forced reduction in household‟s desired consumption based on their location.62 The reduction percentage is derived from a fixed percentage reduction factor, Qi, based on the hours of interruption in that location, scaled by a multiplier, γ which yields the total deficit. Households‟ final electricity consumption is thus given by:

* EEQ  i (7)

The increasing using of backup storage devices among higher income groups is incorporated as a discount to the outage proportion, Q (See Section 3.2 for details).

2.3 Modeling Low Carbon Electricity The low carbon scenario is modeled as an average increase in supply costs corresponding to a decrease in carbon intensity of electric supply of 20 percent below the baseline in 2004-05. Various technology portfolios were chosen to represent a range of additional costs of up to ~$50/ton (see Data Section). The fuel mix for incremental capacity in BAU was assumed to be comparable to the current mix (75 percent coal and 25 percent natural gas, in energy terms). The incremental fuel mix in

61 In practice, the utility categorizes feeders into rationing priority categories. To an approximation, the bulk of the utility‟s administrative divisions (the level at which customer data are available) fall into these categories. See the Maharashtra Electricity Regulatory Commission‟s website (www.mercindia.org.in) for details. 62 Utilities rotate the time of interruption (for e.g., 10-hour outages are alternated during daylight and night hours every alternate day) so that on average in a week households‟ expected loss of consumption does not depend on their load shape.

64 the low carbon scenario comprised 25 percent coal, 45-60 percent gas, 10-25 percent wind and 5 percent solar. Since the focus of this analysis is the distribution of a given cost, the mitigation portfolio was chosen to represent a reasonable cost range, rather than to minimize costs.

The resulting average cost of supply varies from ~Rs 3.5/kWh in BAU to up to Rs ~4.6-5.0/kWh in the low carbon scenarios (depending on actual demand). The additional cost enters into the regulator‟s decision problem in the revenue constraint.

2.4 Price Determination A critical determinant of distributional impacts is that households face different price L M changes (in pe and px) from the same set of primary rates set by the regulator, p g, p g H p g and pind. Note that these changes are modeled as percentage changes over the baseline. In the case of pind, all industrial rates are changed proportionately. The derivation of pe and px from these prices is described below.

2.4.1 Residential Electricity Price pe Consistent with the billing cycle in India, households choose their level of electricity consumption on a monthly basis in response to the grid price of electricity. The average grid electricity price pe, however, depends on the household‟s consumption level, since with the block tariff higher levels of consumption have higher prices. This is a mechanism for income-based price discrimination, where the quantity consumed serves as the proxy for income.

n a n a

L M H Prices pg , pg and pg represent three tiers of the block tariff price structure (Figure 16).63 Note that households‟ average price is a weighted average of their consumption amounts in each tier.

63 Maharashtra has two additional tiers for consumption: above 500 units, and a lifeline rate, for those with consumption below 30 units. Due to currently unavailable data on the number of households who

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Figure 16: Electricity Block Tariff – Maharashtra State Electricity Board, 2004-05

6 5 H pg 4 M pg 3 Avg Price 2 L pg 1 (includes demand charge,

Price (Rs/kWh)Price 0 0 100 200 300 400 Demand (kWh)

Rates include a number of additional fixed and variable charges, including a demand charge, taxes and reliability surcharges. These have been included in the model simulation.

Consumers thus respond to the average price, pe, based on the previous month‟s consumption. Empirical evidence in developed nations suggests average price equally, if not better, predicts consumption than marginal price (Borenstein 2009).

2.4.2 Composite Bundle Price, px

The product bundle price, px, is a reference price of the stylized product bundle consisting of „k‟ consumption categories. However, customers respond to changes in prices only of embedded electricity in these components. The change in px (Δpx) for each household from a change in the industrial price, pind, is given by the product of the price increase of each industry commodity (P’), a matrix that maps these commodities to consumption categories (B), and the consumption shares of each households‟ expenditure in each consumption category (K). Following Neuwahl et al (Neuwahl, Löschel et al. 2008), a modified version of the Leontief Price Model (LPM) is used with the Input-Output tables for the Indian economy to determine commodity price changes (P’) from an exogenous change in price of an intermediate input (electricity). In this approach (as shown below), the A‟ matrix in the LPM is post- multiplied by a diagonal matrix with all 1‟s on the diagonal but for the cell aii

consume in the first category, this tier has been subsumed in the next highest tier. The lifeline rate has been excluded because it is politically unlikely to change under higher costs.

66 corresponding to the electricity sector row-column where the industrial price change is entered as a percentage. The mathematical description of this price derivation is shown below:

Δpx = P’· B. K (8)

1 0 ' 1  P I  A. T V T   pind  0 1

P = an „n x 1‟ vector of percentage price changes of industry-classified commodities. B = an „n x k‟ bridge matrix, with shares of „n‟ industry-classified commodities in the production of „k‟ consumption categories. The base case has equal shares for all constituent commodities in each of the „k‟ consumption categories. K = a „k x 1‟ vector of the expenditure shares of each consumption category k in X.

A’ is the transpose of the „n x n technology coefficient matrix, with aij specifying the expense share of commodity i as input in the production of commodity j. V = an „n x 1‟ vector of factor costs (which remain unchanged in this analysis).

Similarly, the level of industrial electricity consumption associated with household consumption is calculated from the Leontief Demand model (Miller 2009).

Eindirect = ek’· K (9)

Where e’ = b’ (I – A)-1 (10)

And ek’ = e’.B e is the „n x 1‟ vector of the total (direct and indirect) electricity use for the n commodities ek is the „k x 1‟ vector of the total electricity use for the k consumption categories b is an „n x 1‟ vector of direct electricity consumption per unit of expenditure

Eindirect is the final embedded electricity intensity of household consumption expenditure.

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The use of this price model implies that industrial electricity price increases are passed through to consumers, and that no other factor costs change.64 However, industrial consumers do change their source of electricity in response to higher grid prices, since many of them have substitutes for use during interruptions (such as diesel-based self- generation). Thus, the change in the amount of grid electricity that industries purchase in response to a price change is modeled so as to accurately reflect utility revenue impacts and emissions.

∆Egrid = λind. ∆pind.Eindirect

Where λind is the industrial demand elasticity.

2.5 Output Variables The output variables of interest in comparing pricing policies are: the electricity rates L M H set by the regulator ( p g, p g p g and pind), households‟ welfare change, in income terms (Vi ), aggregate social welfare (W), and the poverty-inequality index (S). Aggregate emissions vary for the most part in proportion to aggregate consumption, so they are not presented.65

3 Data All data tables are shown in Appendix B. Three data sources were relied on for this analysis. The National Sample Survey (NSS) of India, 61st round (2004-05) served as the primary data source for household consumption expenditure. However, the NSS survey does not contain reliable electricity consumption data or any data on supply

64 The alternatives to „forward-shifting‟ of electricity price changes would be to allow wages and profits to change in lieu of higher output prices. Examining these income effects would require a Social Accounting Matrix (SAM) that was unavailable here. However, one can surmise that the (minority of) higher income households would face income losses from foregone profits in capital intensive industries while the majority of households would face wage losses in labor-intensive industries. However, the extent to which accounting for these source-side impacts would alter the results of this study depend on the relative magnitude of these impacts. To the extent the impact on capital-intensive industries dominates the regressivity of mitigation impacts estimated here would be exaggerated. Extensive field research would be required to shed more light. 65 The carbon intensity of industrial, diesel-based self generation is marginally different from that of the utility supply fuel mix, causing slight deviation of aggregate emissions from aggregate consumption across scenarios.

68 interruptions.66 Electricity-specific data were collected from a primary survey of 450 households conducted in Western Maharashtra, including the metropolitan areas of Mumbai and Pune (See Appendix D). The NSS data implies that households have an income inelastic electricity budget share of 4-5 percent, which is not only inconsistent with literature, but also underestimates lower income groups‟ budgets. The primary survey shows that the lowest two income groups has electricity budgets of up to 10 percent.67 Finally, embedded electricity expenditure and intensity were calculated based on input-output tables provided for 2003-04 by the Ministry of Statistics and Programme Implementation (MSPI), India.

3.1. Population Stratification Households are delineated by income level and location. However, the granularity of data availability on these parameters limits the number of categories. Six income groups have been created, with the lowest two groups having expenditures below the chosen poverty threshold of $2/day. Average representative households are modeled in each group, distinguished by their average consumption expenditure (from NSSO 61st round), the composition of their consumption bundles, and average electricity budgets. Households in higher income groups have lower price elasticities because of their lower budgets (See Appendix B). Households‟ electricity budgets were differentiated for two seasons, summer (Mar-Aug) and winter (Sept-Feb).

Location determines households‟ supply interruption schedule. The utility defines six reliability regions, A-F. These were aggregated in the model into 3 sections, AB, CD and EF. To a rough approximation, AB fall in urban centers, mostly around the metropolitan areas of Mumbai and Pune, CD fall in urban and peri-urban areas, while EF tend to be rural areas. Due to high income disparities across urban and rural areas, not all regions have all income groups.

66 In the National Sample Survey, households are asked about the last month‟s consumption. This gives inaccurate results, because electricity consumption depends significantly on weather. Since the survey is carried out over the course of the year, reported consumption budgets likely reflect different weather conditions, rather than different preferences. 67 The discrepancy may be attributable to the survey question form, which may lead interviewees to neglect demand charges, which are relative constant with income, and therefore higher budget shares of lower income households.

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3.2. Household Parameters The household parameters used in the analysis are shown in Table B-1 in Appendix B.

The parameters θ, and Qi were estimated from the primary survey. These data were imputed to the broader NSS sample set for Maharashtra based on household consumption expenditure.

The electricity budgets, θ, of survey respondents reflect actual usage, with interruptions. To capture the value share that households place in electricity, budgets of only those households in regions A/B were used to develop an „idealized‟ base case, with no outages. Outages were made endogenous to the analysis so as to estimate their welfare impacts. The survey results show that budgets decrease substantially with income despite a largely progressive residential pricing structure (Figure 16).68

Maharashtra has an unusually elaborate and systematic load shedding protocol compared to other states. Survey results verified that the average duration of outages experienced by households matched utilities‟ schedules. The percentage loss in consumption, Q, was estimated to be the same as the percentage of hours of interruption, adjusted for any backup devices. The survey revealed that less than 15 percent of households had any form of backup, other than kerosene lamps, and that most high-income households that face interruptions tend to have backup storage devices. Thus, Q was set to zero for income groups 5-6, discounted for middle-income households (3-4) in regions C-F, while all other households were assumed to be fully exposed to outages. Values of Q were subject to sensitivity analysis.

The elasticity of substitution (σ) was chosen so as to obtain price elasticities for electricity (based additionally on the observed electricity budgets from the survey) that fell within the range observed in literature. Estimates of household price elasticity vary widely, particularly for India, from -0.05 to -0.65 (Khanna and Rao 2009). Since this

68 The survey also revealed that low-income households have a higher standard deviation in their electricity budgets. Even among the poor, welfare impacts would be highly heterogeneous. Parsing out determinants of these differences is an important area for further research.

70 is a short-run analysis, when elasticity tends to be fairly low, a range of -0.2 to -0.3 were used. This was varied in the sensitivity analysis.

Table B-2 in Appendix B shows all the components of the electricity block tariff structure. Notably, the price is a two-part tariff, including a fixed and variable cost, and additional surcharges and taxes. In this analysis, the regulator changes only the variable component of price. This matches actual price setting behavior and is economically efficient, since it influences consumption behavior. It also forces a price floor of ~Rs 1/kWh, which is unlikely to constrain regulatory pricing behavior.

Finally, the expenditure shares of different household goods and services (K) are shown in Table B-3 in Appendix B. The main observation is that lower income households spend a higher percentage of their expenditure on food, while the highest income households spend a high share on services and durables. Their relative electricity intensity, therefore, affects the indirect price impacts. Households‟ consumption bundles were mapped to the 130 industries based on the mapping used by Pachauri (Pachauri 2007) with some modifications.

3.3. Input-Output and Industrial Demand The latest Input-Output tables available for India (2003-04) were used to obtain the A matrix (Equation 8) to calculate the price changes and embedded electricity consumption for each commodity (vector e in Equations 9, 10). To convert electricity expenditure to electricity flows, weighted average industrial electricity prices across states were calculated for the benchmark year69 and used to infer the embedded electricity consumption in the production of each commodity. The national weighted average prices in 2004-05 were Rs. 0.75/kWh for agriculture, Rs. 4/kWh for industry (manufacturing) and Rs. 5/kWh for commercial sectors (services), which were used in the base case.70

69 The I/O tables are specified for the national economy. They are unavailable for individual states. 70 Data provided by Prayas Energy Group, compiled from the Central Electricity Authority of India databases.

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The industrial demand elasticity to price was set at -0.5 in the base case. In India, elasticity tends to be high because of a history of charging high industrial rates. Price elasticities of different industrial sectors in India have been estimated at a wide range of -0.05 to -1.35 (Khanna and Rao 2009), and in particular instances, over 2.0 (Chattopadhyay 2004).

3.4 Electric Generation Costs Actual supply costs were obtained from utility tariff orders filed with the Maharashtra Electricity Regulatory Commission (MERC), while low carbon technology costs were obtained from other government and NGO sources. These are shown in Table B-4 in Appendix B. The average cost and emissions for the generation fleet with and without mitigation were calculated as a weighted average of fuel types, weighted by generation share. These costs include transmission and distribution losses of 29 percent, which include both physical losses and theft.

3.5. Baseline Calibration The base case simulates actual conditions in Maharashtra in 2004-05 to the best extent possible. The main challenge and limitation in doing this is the inconsistency between data sets, primarily between the NSS data and utility electricity data. First, NSS survey data, as with most household consumption surveys, underestimates consumption expenditure, by a margin that increases with income. Previous research reveals that NSS data consistently captures around 60 percent of private final consumption expenditure reported in national statistics(Pachauri 2007). Further, in addition to final household demand, electricity is used for capital formation, government consumption and trade, which are not captured by household consumption expenditure.

Thus, residential electricity sales are entirely reproducible in the baseline, but with indirect consumption included, approximately half the total consumption was reproduced. Available supply was scaled down to reflect actual levels of supply scarcity and interruptions to households. The simulation exercise was conducted on this scaled-down representation of Maharashtra‟s electricity system.

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4 Scenarios The following list summarizes the scenarios presented:

1 Idealized Base case (current prices, no outages) 1.a Status quo (current prices, with outages) 1.b Economic Efficiency (optimal prices, with outages) 2 Low Carbon Scenarios 2.a Economic Efficiency Objective 2.b Equity Objective 2.c Equity objective with Cost of Supply constraint 3 Sensitivities 3.a Industrial price elasticity (Low) 3.b Industrial price elasticity (High) 3.c Energy efficiency investments (Economic Efficiency scenario) 3.d Energy efficiency investments (Equity scenario)

Ten scenarios are presented, three under current costs, three under the $50/ton low- carbon scenario, and four sensitivities. The benchmark scenario against which all welfare changes are assessed is a simulation of actual demographic and sectoral conditions in Maharashtra in 2004-05, but for one change. In order to endogenize outages, supply is assumed to be adequate, such that households consume their desired level of demand. This enables an estimate of the relative distribution of the welfare losses due to outages under the second scenario, which represents status quo conditions. It is worth reiterating that the absolute values of outage costs shown are underestimated, since they exclude other, far more important, but unquantifiable, welfare impacts. A third scenario under current condition is run to determine the efficiency gains (losses) from pricing to maximize aggregate welfare. This indicates the direction of pricing reform what would benefit society in the absence of mitigation.

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For the low carbon scenario, two „optimal‟71 pricing scenarios were run for the Economic Efficiency and Equity social welfare functions (SWF). In addition, the scenarios were each run with the cost of supply mandate added as a constraint. However, the results of the economic efficiency scenario did not change with the inclusion of this constraint, so it is not presented. Lastly, sensitivity analyses for the most critical variable have been presented: industrial price elasticity (which, in practice, includes industries‟ migration to captive generation, among other changes in production), and energy efficiency investments.

5 Results and Discussion This section presents first the intermediate findings of indirect impacts of industrial price changes. The pricing and welfare distributional impacts are presented next, followed by sensitivities of industrial elasticity.

5.1 Intermediate Findings: Indirect Consumption Effects The indirect budget impact of a change in industrial prices is relatively invariant with income. However, for the top 1-2 percent of the population, the relative budget impact of industrial electricity price changes exceeds the impact of residential price changes (Figure 17), for a given supply cost. (Note that households in income group 1 spend 9- 10 percent on direct electricity, while those in income group 6 spend 3-4 percent.) This result depends on four factors: households‟ expenditure on goods other than electricity, the mix of goods they purchase, the relative electricity intensity of production of these goods, and electricity prices in these sectors.

71 These are optimal only in the sense of the best selection of electricity price levels to achieve the chosen regulatory objectives. Broader consideration of other policy instruments (such as other commodity subsidies or taxes) may have welfare implications that are not considered in this paper.

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Figure 17: Residential and Industrial Price Impact Comparison

6.0% 5.0% Residential Price Impact Industrial Price Impact 4.0% 3.0% 2.0%

Percent of Income of Percent 1.0% 0.0% 1 2 3 4 5 6 Income Groups (1= Lowest)

Price impacts reflect price changes required separately for residential and industrial prices to recover a 10 percent increase in supply costs. The following observations about these factors explain the relative budget impact of industrial price changes on households:

 Industries exhibit a wide range of electricity intensities (Figure 18). Food cereals have the highest, and services the lowest intensity.  Households exhibit significant differences in their consumption bundles (Table B- 3 in Appendix B), with poorer households consuming more on foods, and higher income households consuming more services and durables.  The net effect is that the indirect electricity intensities of households (electricity use per unit of expenditure) decreases with income (Figure 19). This result is consistent with other findings on carbon intensities and expenditure elasticities of energy across households (Murthy, Panda et al. 1997; Pachauri 2004).  Electricity prices to agriculture are significantly subsidized, so the expenditure share of electricity in food industries is relatively low. This benefits lower income groups more, leading to the final result that the budget impact of price shocks increases marginally with income.

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Figure 18: Industry Group Electricity Intensities (2003-2004)

0.12 0.1 0.08 0.06 0.04 0.02

0 Electricity Intensity Electricity Intensity (kWh/Rs)

Uncertainty bands correspond to 25 percent variation in baseline electricity prices in 2004-05 Underlying I/O tables from Ministry of Statistics and Programme Implementation, India, 2003-04.

Figure 19: Household Expenditure Electricity Intensity – by Income Group

0.06 0.05 0.04 0.03 0.02 0.01 0 Embedded Electricity Electricity Embedded (kWh/Rs) 1 2 3 4 5 6

Income Groups (Lowest = 1)

Uncertainty bands correspond to 25 percent variation in baseline prices in 2004-05 Underlying I/O tables from Ministry of Statistics and Programme Implementation, India, 2003-04. 5.2 Distributional Impacts of Price Scenarios Table 4 and Table 5 summarize the prices and output metrics for these scenarios. All welfare changes in Table 5 are relative to the idealized base case, where supply is sufficient to permit uninterrupted supply (Scenario 1a). Appendix B has a detailed breakdown of the results for all income groups. As a point of reference, total revenues from electricity sales in this scaled down simulation of Maharashtra are ~Rs. 13,000

76 crores (130 billion), and total household expenditure was in the range of Rs. 80,000 crores in 2004-05.

5.2.1 Baseline Scenario Results Two findings are noteworthy. First, under current conditions it would be economically efficient and more equitable (Table 5, row 1c) to raise industrial prices and reduce the lowest price tier to zero (Table 4, row 1c). This is because the industrial price increase affects the highest income group the most. Due to large number of lower income households, the welfare gain to all income households from lower residential rates offsets the welfare loss that accrues primarily to the highest income households from higher prices of other goods. If this outcome is robust to broader economic considerations,72 the current trend of moving all prices towards cost of supply would be counterproductive.

Table 4: Optimal Prices – Baseline and Low Carbon Scenarios

Industrial Residential Residential Residential Price Scenarios (2004-05) Price (Low Tier) (Mid Tier) (High Tier)

Rs/kWh Rs/kWh Rs/kWh Rs/kWh

Baseline Scenarios

1a. and 1b. Status Quo Pricing 4.80 2.05 3.9 5.60

1c. Optimal - Economic Efficiency Objective 6.10 0.00 4.4 4.40

Low Carbon Scenarios

2a.Economic Efficiency Objective 6.63 3.56 5.34 5.34

2b. Equity Objective 7.32 1.00 40.00 40.00

2c. Equity with Cost of Supply Constraint 6.02 3.47 15.00 15.00

Note: Benchmark industrial electricity price is a weighted average, including demand charges, for agricultural, industrial and commercial rates. See Appendix B for the average residential rates paid by different income groups, which is a weighted average of the tier prices, based on their consumption level, plus demand charges and surcharges.

72 The efficiency losses from higher costs of industrial capital expansion are not considered here.

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Second, rural households who face severe supply interruptions today are no worse off than under the low carbon scenario. These households‟ minimum welfare losses from interruptions are 4-5% of their expenditure, which is comparable to their losses under higher costs, even when they pay full average costs (5-6%). This is because with higher prices demand contracts sufficiently to obviate the need for supply interruptions. As noted earlier, this analysis captures the welfare loss of only a suboptimal allocation of expenditure to different goods. If the unquantifiable welfare impacts of rationing were considered, this group – in regions EF - may well be better off than today with higher average prices.

Table 5: Welfare Metrics – Baseline and Low Carbon Scenarios

Agg HH Sen Met Unmet Welfare Avg Cost Poverty Output Metrics (2004-05) Demand Demand Change of Supply Index1

GWh (GWh) (Crores2 Rs.) (Rs/kWh)

Baseline Scenarios

1a. No Rationing (Idealized) 45,468 - 0.00 3.5 0.282

1b. Economic Rationing (Status Quo) 43,790 1,678 -402 3.5 0.282

1c. Economic Efficiency objective 43,458 2,744 1,265 3.5 0.278

Low Carbon Scenarios

2a.Economic Efficiency (EE) Objective 40,389 - -3,673 4.9 0.286

2b. Equity Objective 39,130 - -6,500 5.0 0.283

2c. Equity with Cost of Supply Constraint 40,257 - -4,540 4.8 0.285

The index measures income inequality with respect to a poverty threshold: the higher the index, the more welfare losses (from price changes) are distributed towards poorer households. 1 Crore = 10 million.

5.2.2 Low Carbon Scenario Results With higher average costs, there is a trade-off in pricing policies between equity and efficiency, as reflected in the inverse relationship between welfare gains and the poverty-inequality index (Table 5, rows 2a-b). The aggregate household welfare loss is in the range of 3,600 crores, or 4-5 percent of residential expenditure, in the efficiency

78 scenario, and an additional 3,000 crores, or an additional 3.7 percentage points, in the equity scenario.

This trade-off differs from the baseline cases, because low carbon costs cannot be recovered by raising industry rates alone. Indeed, with the baseline elasticity of -0.5, raising industrial prices above Rs 7/kWh does not increase revenues. Residential rates have to be increased. The most efficient way to recover costs from residential rates is to lower the upper tier rate, while raising the lower tier rates. It is noteworthy that doing so raises all households‟ average rate above the average cost of power (Figure 20). Households face welfare losses of 3.4-6.2% of their monthly expenditure (Figure 21). However, this price impact is regressive, as lower income groups suffer welfare losses that are a higher percentage of their monthly expenditure than that of higher income groups.

Figure 20: Average Residential Prices - Low Carbon Pricing Scenarios

14.00

12.00

10.00

8.00

6.00

4.00

Avg Residential Avg Rate(Rs/kWh) 2.00

- 1 2 3 4 5 6 Income Groups (Lowest = 1)

To minimize the welfare losses to the poor, the lowest residential tier price would have to be reduced, while upper tier and industrial prices would have to be raised. In this Equity scenario, the income groups below the poverty threshold (groups 1 and 2) can be virtually insulated from welfare losses.

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Figure 21: Distribution of Welfare Losses - Low Carbon Pricing Scenarios

20.0% 2b. Equity

16.0% 2c. Equity with Cost of Supply Constraint 12.0% 2a. Economic Efficiency 8.0%

Welfare Loss Loss Welfare 4.0%

0.0% (% of Monthly Expenditure) Monthly of (% 2,000 4,000 6,000 8,000 10,000 50,000 1,500 3,000 4,200 7,000 -4.0% Region AB (Urban) Region EF (Rural) Income Group (Avg Monthly Expenditure)

Therefore, the block tariff adequately allows for redistribution (cross-subsidies) within the state to shield the poor from higher electricity costs, including indirect costs. However, the price changes required in upper tier blocks may be politically infeasible. Higher income households‟ welfare impact doubles to 12-17% of their monthly expenses. Low income groups pay rates of Rs 2-3/kWh, middle income groups pay Rs 10-12/kWh, and income group 6 (the richest) pay average rates of Rs 33/kWh, which is likely to be politically infeasible. A caveat here, though, is that the revenue constraint drives the need to increase prices to this level. If a revenue loss of even 500 crores (4%) is acceptable – funded by a government subsidy, for example - middle and upper tier price can be reduced from Rs 40/unit to Rs 30/unit, which would reduce income group 6 average prices to ~Rs 25/kWh. This is because at such high price levels, revenue gains (losses) are relatively inelastic to price changes.

In all cases, equalizing the middle and upper tier prices is efficient. This is because the highest income group – the only group that pays the upper tier price - suffers the highest welfare losses from price increases. Shifting part of this burden to middle income households by raising middle tier prices instead increases efficiency.

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Combining efficiency and equity objectives predictably leads to a compromise between the two objectives on their own, in terms of the average prices paid (Figure 20).

The demand elasticity of industrial consumers is by far the most influential parameter for the distribution of household welfare impacts. The change in the structure of pricing in going from the baseline to the low carbon scenario is a result primarily of the inability to recover higher costs from industrial customers‟ revenues. If elasticity were lower, the structure of pricing may not have to change under low carbon pricing. Alternatively, if industrial elasticity is higher, then the efficiency/equity synergy may be overstated in the baseline scenario. This sensitivity is explored next.

5.3 Sensitivity Analysis – Industrial Price Elasticity Sensitivity analyses were conducted on several uncertain parameters: low carbon costs, household electricity budgets (which influence their price elasticity), industrial price elasticity, and population distribution across income groups. All these uncertainties affect absolute price and welfare impact levels. However, of these, industrial price elasticity significantly influences the distributive effects and related optimal prices. In keeping with the focus on distribution, the results of the latter effect are shown in Table 6 and Table 7, relative to Scenario 2a, the Economic Efficiency scenario for the low carbon costs.

Table 6: Optimal Prices – Industrial Elasticity Sensitivity

Industrial Residential Residential Residential Price Scenarios (2004-05) Price (Low Tier) (Mid Tier) (High Tier)

Rs/kWh Rs/kWh Rs/kWh Rs/kWh

2a.Economic Efficiency Reference (-.5) 6.63 3.56 5.34 5.34

3a. Low Industrial Elasticity (-.3) 7.4 0.00 12.0 12.0

3b. High Industrial Elasticity (-.75) 5.7 5.27 5.3 5.3

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Table 7: Welfare Metrics – Industrial Elasticity Sensitivity

Agg HH Sen Met Unmet Welfare Avg Cost Poverty Output Metrics (2004-05) Demand Demand Change of Supply Index2

GWh (GWh) (Crores Rs.) (Rs/kWh)

Low Carbon Scenarios (Economic Efficiency)

2a.Baseline Elasticity (-.5) 40,389 - -3,673 4.9 0.286

3a. Low Industrial Elasticity (-.3) 45,800 - -1,429 4.5 0.279

3b. High Industrial Elasticity (-.75) 39,433 - -4,495 4.9 0.288

The sensitivity analysis results suggest that trade-offs between efficiency and equity from low carbon growth in electricity depend on the extent to which utilities can recover supply costs from industrial customers. If industries face market (high prices of substitutes, like diesel) or regulatory (penalties for exiting the grid) barriers to self- generation, and as a result of which have a lower price elasticity, the utility can recover more revenues from industry, and price residential rates lower. It would then be both efficient and equitable to lower residential rates and raise industrial rates. If, on the other hand, barriers to migration reduce such that elasticity is -0.75, industrial rates can be increased only to Rs 5.7/kWh (on average), which requires that residential rates increase substantially to recover costs. The household welfare losses from low carbon in this case are higher, and the poverty-inequality index is worse (higher), since most households face higher losses from direct residential price increases.

This sensitivity analysis reflects the potential for stranded costs of low-carbon resources, if diesel and other fuels are more competitive relative to grid electricity. If low-carbon investments arise from climate-related policy, these policies should include all fuels so as to avoid such leakage.

5.4 Sensitivity Analysis – Energy Efficiency Investments Appendix C shows the assumptions and method used to simulate household decisions that include energy efficiency investments. The results of this sensitivity analysis indicate that the optimal tariff design changes marginally with energy efficiency

82 investments. However, the absolute welfare impact changes by up to 15 percent, in aggregate terms and for the investing household groups. Details of the differences in results are shown in Figures 22 and 23 for the main scenarios – Economic Efficiency and Equity.

The patterns of investment are predictable. In the efficiency scenario, all households but those in the highest income groups (5-6) invest, as they have the greatest incentive. On the other hand, in the equity scenario, only the highest income groups (5-6) find it economical to invest, since they face the highest electricity prices. There are regional differences as well, where households in the same income group may not invest in rural regions, where their electricity usage is lower due to supply interruptions.

As shown in Figure 22 and Figure 23, the adverse distributional impacts from mitigation are ameliorated. Aggregate welfare losses are reduced from 3,600 to 3,000 crores in the efficiency scenario (Table 8) and from 6,000 to 5,000 crores in the equity scenario (Table 9).

As such, this sensitivity shows that energy efficiency investments do not alter the fundamental conclusions of the prior analysis, but that welfare losses may have been overestimated by up to 15 percent.

Figure 22: Distribution of Welfare Losses - Energy Efficiency Sensitivity for the Economic Efficiency Scenario

7.0% Baseline 6.0% With Energy Eff 5.0% 4.0% 3.0% 2.0% 1.0% 0.0% 1 2 3 4 5 6 1 2 3 4 Region AB (Urban) Region EF (Rural)

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Table 8: Optimal Prices - Energy Efficiency Sensitivity for the Economic Efficiency Scenario

Industrial Residential Residential Residential Price (Low Tier) (Mid Tier) (High Tier) Rs/kWh Rs/kWh Rs/kWh Rs/kWh 3c. With Energy Efficiency Investments 6.59 3.53 5.33 5.33 2a. Baseline 6.63 3.56 5.34 5.34

Figure 23: Distribution of Welfare Losses - Energy Efficiency Sensitivity for the Equity Scenario

16.0% 14.0% Base Case 12.0% 10.0% With Energy Eff 8.0% 6.0%

4.0% WelfareLosses 2.0% 0.0% -2.0% 1 2 3 4 5 6 1 2 3 4 Region AB (Urban) Region EF (Rural)

Table 9: Optimal Prices - Energy Efficiency Sensitivity for the Equity Scenario

Industrial Residential Residential Residential Price (Low Tier) (Mid Tier) (High Tier) Rs/kWh Rs/kWh Rs/kWh Rs/kWh 3.d With Energy Efficiency Investments 6.85 1.20 39.90 39.90 2.b Baseline 7.32 1.00 40.00 40.00

5.4.1 Robustness This analysis calculates the maximum likely impact from energy efficiency investments. In particular, investments by households were if anything overestimated. This is for at least two reasons. First, efficiency investment costs are likely to be higher due to hidden or transaction costs. Their importance is evidenced by the finding that if investment costs were marginally lower, the model predicts that households would invest heavily in the baseline, which in reality they do not appear to do. Second,

84 households were assumed to replace their entire stock of appliances, if economical. In reality, they may replace appliances based on their age.

Differences in access conditions to markets or in investment elasticities across income groups would also yield investment amounts within the range bounded by this analysis, assuming that investment costs indeed cannot be lower. Consider the equity scenario, where only high income households invest. If they were less elastic than assumed, or faced higher costs, investments would be lower, and the results would look more like the baseline, where households make no changes to their appliance stock. Investment is prohibitively expensive for low income groups in the equity scenario, since their rates aren‟t high enough to justify investment. So access and elasticity are irrelevant to their decision in this case. The same logic applies in the efficiency scenario, with the groups reversed. Wealthy households who do not find it cost-effective to buy new appliances on the basis of appliance costs alone would not be affected by having lower transaction costs or higher elasticity to price changes. Lower income households‟ investments have been maximized, so higher transaction costs and lower elasticities would only make their welfare impacts look more like the baseline case.

5.4.2 Caveats The model predicts a minor rebound effect that leads to a net increase in electricity consumption for households that invest. This rebound effect may be exaggerated for low income households, because households likely exhibit asymmetric price elasticity due to physical consumption constraints that the model does not include (e.g., sufficiently low operating costs of energy efficient lighting induces consumption that would imply that lights are used in the daytime).73

This caveat implies that the benefits of investment perhaps have been overestimated for low income groups in the efficiency scenario.

73 Note, the CES utility function exhibits diminishing returns to increasing consumption at a given income level. This effectively does (correctly) generate asymmetric price responses. However, external constraints are not factored.

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6 Conclusion This study reveals that the distributional impacts of climate change mitigation policies cannot be specified without characterizing regulators‟ pricing behavior, which in turn reflects other policies and constraints in the electricity sector. Regulators‟ flexibility to distribute mitigation costs across households depends on their ability to rely on revenues from industrial customers. To the extent industrial migration prevents this reliance, regulators can still shield low-income groups from mitigation costs by lowering their residential rates, but doing so requires politically difficult rate increases to higher income groups and aggregate welfare losses. On the other hand, if regulators can rely more on industrial rate increases to recover costs, they can shield lower income groups without aggregate welfare losses or other drastic rate increases. Lower income groups in certain rural areas may indeed benefit from mitigation because of the possible reduction in supply interruptions that is enabled by reduced demand from higher electricity prices. If the current trend of keeping industrial rates low is counterproductive under broader economic considerations, then the adverse distributional effects of mitigation may be exaggerated by prior governance failures. This finding emphasizes the importance of defining the baseline institutional conditions against which to evaluate distributional impacts of mitigation policies.

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CHAPTER 4 – IMPLEMENTING AN EXEMPTION FOR THE POOR IN INTERNATIONAL CLIMATE AGREEMENTS

1 Introduction As discussed in Chapter 1, the current global greenhouse gas (GHG) emissions trajectory puts us on a path of almost certainly exceeding an average global temperature rise of 3 degrees C above preindustrial levels. Developed and developing countries continue to search for common ground on how to share responsibility to meet the 2 degrees C (“2C”) target set in the Copenhagen Accord, even though negotiating parties have put forth various formulations of fair burden-sharing as far back as the first Conference of Parties in 1995 (Cazorla 2000; Ringius, Torvanger et al. 2002). Chapter 1 laid out the logic for a establishing a minimal moral basis for burden-sharing agreements that would exempt individuals below an income threshold from mitigation burdens. On this basis, certain large developing countries, such as the BASIC countries,74 would have both a mitigation obligation and an exemption for the poor. This chapter examines the challenges of implementing this type of an exemption.

Let‟s recap the motivation and basis for this type of an exemption. Scholars of global climate justice have largely assumed states to be the primary agents of benefits and duties (the “statist” view). A few scholars emphasize the importance of protecting individual‟s basic rights in a fair distribution (the “cosmopolitan view”) regardless of people‟s citizenship (Baer 2008; Caney 2009; Caney 2010). However, state governments have considerable agency over the outcomes of these agreements. States‟ roles in the implementation of any burden-sharing arrangements has been acknowledged as a lacuna in literature (Grasso 2007). But the specific concerns of states implement burden-sharing agreements that aim to protect individual interests

74 Brazil, South Africa, India and China. 87 have been largely ignored.75 This paper attempts to fill this gap. Understanding institutional influences within states is critical not only to contemplate additional duties that parties need to define in order to successfully implement such exemptions in climate agreements, but also to understand the incentives these conditions create for defining the terms of such an exemption.

The moral basis for the exemption is that the imposition of mitigation burdens, however derived, would impinge on individuals‟ basic needs or rights. That is, up to a certain threshold of entitlement, people ought to be exempt from any substantive mitigation obligations for which they are not compensated (Ringius, Torvanger et al. 2002). In the climate literature, this threshold has been interpreted in terms of “subsistence emissions” (Shue 1993; Jiahua 2004), or a decent standard of living (Caney 2009), among others. While the basis for and measures of individual entitlements to such an exemption are controversial and debated, the principle has wide appeal, since an exemption entails that mitigation should not interfere with others‟ ability to fulfill their basic rights, but it raises no obligation to meet them (Shue 1999). As such, at a certain level of abstraction it can be thought of as a „minimal‟ and relatively less controversial moral basis for allocating mitigation costs than other distributive principles. It also has political relevance, since individual entitlements (such as, to per capita emissions) have indeed formed one basis of developing countries‟ justification for differentiating mitigation burdens.76

This approach of exempting from mitigation poor populations within states, has been proposed before using different threshold definitions (Baer 2008; Chakravarty 2009;

75 Scholars have written about the incentives for population growth created from „per capita‟ emissions entitlements (See Singer 2002). However, this is easily surmounted by fixing the base year used in allocating emissions. The range of policy issues presented here is wider, and less easily surmountable. 76 “Resource-sharing” views, such as per capita emissions entitlements, can also be interpreted as a type of exemption entitlement, though they are typically invoked to justify how states, rather than individuals, should share scarce resources. See Singer, P. (2002). One World: Ethics of Globalization. or Vanderheiden, S. (2009). Atmospheric Justice: A Political Theory of Climate Change. New York, Oxford University Press. However, others have criticized this view for not necessarily protecting the fundamental interests of the disadvantaged. On this, see Hayward, T. (2006). "Global Justice and the Distribution of Natural Resources." Political Studies 54: 349-269.and Gardiner, S. (2004). "Ethics and Global Climate Change." Ethics 114: 555-600. 88

Muller and Niklas Hohne 2009).77 However, these proposals treat states as passive and ideal implementation agents.

I focus on one set of implementation issues, related to the influence of states‟ internal policies and institutions on the terms and outcomes of agreements based on such an exemption. Translating an exemption principle into climate policy presents many practical and ethical issues. Many of these issues overlap with, and indeed are the subject of, the broader distributive justice and human rights discourse, as discussed in Chapter 1.78 However, state‟s internal policy choices would influence the distribution of populations around an exemption threshold, regardless of how this threshold is defined and set.

That states‟ internal policies and institutions can influence the success of such agreements raises new questions about how to distribute the burdens of exempting the poor.79 I raise two issues for consideration. First, state policies towards poverty alleviation and the poor‟s energy choices in emerging economies can materially change the exempted poor‟s emissions and in turn the cost of exemption. If this influence is materially significant, to what extent should states that benefit from an exemption be held accountable for these policy influences? Second, the difficulty in targeting mitigation policies to particular income groups in emerging economies make it likely that the poor bear mitigation burdens even when states‟ mitigation obligations are reduced so as to exempt them. Should other states have an obligation to ensure that

77 There are other burden-sharing proposals that allocate mitigation costs to states on the basis of their responsibility (cumulative or annual emissions), capacity (GDP) or emissions intensity (emissions per unit GDP) without adjusting for an exemption to particular populations. These are not the focus of this paper. 78 See Caney, S. (2009). Climate Change, Human Rights, and Moral Thresholds. Human Rights and Climate Change. S. Humphreys, and Mary Robinson. Oxford, Cambridge University Press.for a broader discussion and set of references on this literature. 79 Both these objections have been raised in the broader context of international distributive justice, but not in the climate justice literature. The emphasis there is on whether these objections undermine the claims that people have duties to alleviate poverty in foreign states, rather than to address how to manage both sovereign and international duties. See Chapter 6: International Distributive Justice in Altman, A., and Christopher Wellman (2009). A Liberal Theory of International Justice. Oxford, Oxford University Press. 89 the poor actually benefit from an exemption? I offer some resolution to these questions.

From a practical standpoint, the incentives that agreements create for parties‟ own interests are important to design feasible climate policies (Victor 2007). The possibility that states‟ internal policies can influence the distribution of mitigation burdens among countries would, in principle, create a conflict among parties over the terms of an exemption. I briefly discuss these conflicts, and provide preliminary thoughts towards their resolution.

In the next two sections I discuss each of these two issues: the subjectivity in what emissions qualify for an exemption; and the enforcement of the exemption. In both sections, I first illustrate the empirical conditions that give rise to these concerns, using India as a case. I then discuss the ethical implications for designing a burden-sharing framework. Lastly I discuss the obstacles and potential remedies for getting agreement on the terms of an exemption for developing countries.

2 Policy Influence and the Exemption Burden Burden-sharing proposals that allow for the poor‟s exemption implicitly assume that all states equally share in the cost of this exemption, (call this the “external exemption burden”) even though beneficiaries are concentrated in particular countries. In this section, I ask whether this is a reasonable assumption in practice. I first discuss briefly how policy influences the poor‟s emissions and the cost of exemption. I then give a concrete illustration of the financial stakes parties to an agreement would have in this policy uncertainty for the case of India.

2.1 Relying on a Baseline of Income Distribution The external exemption burden depends, in the least, on the number of poor, their income and the carbon intensity of their income. For a given poverty threshold, it is likely that in the next two decades – the time frame within which mitigation commitments are imperative to achieve emissions stabilization – a large number of poor will not graduate out of poverty (consider that India has over 750 million people

90 who earn under $2/day).80 The rate and distribution of growth therefore matter for quantifying the external exemption burden. On what baseline of poverty should the exemption burden be based? Note, there are a number of practical issues in designing agreements that require data on internal economic and social indicators, such as quantifying and verifying data under uncertainty.81 However, here I am concerned strictly with the policy influence over actual income distribution and poverty levels, setting aside these data issues. The more future growth lifts people out of poverty, the smaller the population that will require exemption. There are, quite obviously, numerous factors, both within and outside of policymakers‟ control, that influence poverty alleviation. The aggregate income of the poor depends at least on GDP growth, population control policies, and a wide range of policies that affect poverty, including rural employment, agricultural policy, expanding access to services to the poor, and the like.

In addition, there is also policy uncertainty around the extent to which governments can reduce the carbon intensity of the poor‟s income growth by exploiting „co- benefits‟ between mitigation and development. As discussed below, while volumes have been written on the need and guidelines for sustainable development, far less is understood about the institutional barriers that stand between well intentioned policies and their implementation.

The problem is that these policies fall squarely within states‟ sovereign rights, yet they influence the cost that other countries bear to shield the poor from mitigation costs. Before discussing the ethical dimensions of this issue, I illustrate the problem with an example, and provide a practical sense of the financial stake of policy uncertainty on burden-sharing, using India as a case.

80 World Bank Poverty database. 81 Other types of uncertainty that matter for monitoring and verification, but are not addressed here, include the validity of metrics, the accuracy of data and of future predictions. See Page, E. (2008). "Distributing the burdens of climate change." Environmental Politics 17(4): 556-75. 91

2.2 Financial Stake of Policy Uncertainty in Exempting the Poor Consider a hypothetical burden-sharing arrangement between two countries, an Annex I country (A1) with no poverty and a non-Annex I country (NA1) with substantial poverty, that have a commitment to exempting the poor from mitigation. Suppose they assess their respective responsibility to exempt the poor based on their aggregate income share above the poverty threshold (e.g., $15 trillion and $2.5 trillion, for a 82 poverty threshold of $9/day). Suppose that NA1 has 500 million tons of CO2eq below the threshold in 2015, and is required to reduce emissions by 30 percent below BAU in 2020. NA1 can follow two “baseline” development trajectories for the next decade, A and B, with the same average GDP growth, but different internal emphases in development. In A, rural development is emphasized, the state pursues an improved biomass cook stove program that benefits the poor‟s development and reduces their carbon intensity. In trajectory B, rural development is neglected and the rural poor shift to modern, carbon-intensive fuels. Trajectories A and B lead to aggregate emissions of 250 million and 350 million below the poverty threshold. The costs that A1 would bear to exempt the poor in the two scenarios, A and B, would be $2 billion and $3 billion, with a mitigation cost of $35/ton. Should NA1 bear a higher share of the extra $1 billion than A1, by virtue of having greater accountability for the chosen development path?

For a handful of emerging economies with substantial income and relatively large poor populations, uncertainty in intranational income distribution may have a considerable financial impact on mitigation agreements of this kind. India, Indonesia, China, Brazil, and Russia stand out as such examples, although they vary in the extent of each (Figure 24). That they have relatively high emissions from their poor suggest that they would be entitled to significant resources to share in the cost of the poor‟s mitigation exemption. That they also have relatively high non-poor income suggests that parties to an agreement would be concerned that those states have substantial resources to devote to poverty alleviation (consider how willing developed countries

82 This is indeed the approach used in Greenhouse Development Rights (Baer et al 2008). 92 would be to give resources to China, as opposed to Indonesia, even though China has far more poor people than Indonesia).

Figure 24: Country Intranational Income and Emissions Distribution: 2007

2000 1800 (Illustrative) Poverty Threshold: $PPP 9/day9/day China $3300b,3144 MT 1600 Indonesia 1400 India

1200 eq)

10002

CO 800 600 400 Brazil Russia

Poor Emissions (Mil Tons Tons (Mil Emissions Poor 200 Mexico 0 0 500 1000 1500 2000 Non-Poor Income (PPP$Bil.) Data: Greenhouse Development Rights Calculator

The financial stakes in an actual agreement of this kind would be in the range of several billion dollars. Consider the case of India, with the same target of reducing emissions by 30 percent below BAU by 2020. Using a stylized model of India‟s income distribution83, I examine the sensitivity of external states‟ obligation to exempt the poor in India, for a poverty threshold of $9/day. This threshold corresponds roughly to the poverty line for Uruguay (adjusted for purchasing power parity), which is among the highest of developing country poverty lines (Ravallion 2009) and lies well within the bound of thresholds used in other proposals.84 Generally, developing countries‟ poverty lines tend to reflect only abject poverty, since most countries tend to define their own poverty lines based on relative, rather than absolute, levels of poverty (Ravallion 2002). As mentioned, the subject of how to define a poverty threshold is a subject of much research. Here, I only illustrate its practical importance.

83 The model uses a log-normal representation of income distribution to estimate the income around a threshold given a Gini coefficient, threshold level and per capita GDP. (See Lopez, J. H., and Luis Servén (2006). A Normal Relationship? Poverty, Growth and Inequality, World Bank.). 84 GDR (Baer et al 2008) uses a threshold of ~$20/day, while Chakravarty et al (2009) use a 1 tCO2 per capita, which varies by country in income terms, but was in the range of $5/day in 2003. 93

I illustrate deviations in BAU with three macroeconomic indicators that serve as proxies for policies that influence the future distribution of income around an exemption threshold: GDP growth; Gini coefficient (income inequality); and population growth. 85 I calculate the external mitigation burden of exempting the poor (that is, the „debt‟ owed to India) based on the Indian rich‟s share of global income of 2.2% in 2010, and a mitigation cost of $35/ton. 86

Table 10 shows that with uncertainty in these indicators the aggregate external exemption burden for the next ten years can vary between $47 billion and $56 billion. In particular, higher (lower) inequality combined with higher (lower) population growth and lower (higher) GDP growth can increase (decrease) the external exemption burden by up to $5 billion around BAU estimates. 87

Table 10: Exemption Costs Under Alternate Development Paths in India

2010-2020 BAU Path A (Low Poverty) Path B (High Poverty) Mitigation Target 30% below BAU by 2020 (All Scenarios) GDP Growth 7% 8.5% 5.5% Population Growth 1.4% 1.15% 1.65% Gini Coefficient 0.37 0.32(2020) 0.42(2020) External Burden (Poverty $53.5b $47.5b $56.3 b Threshold $9/day) Assumptions: GDP and population growth, actual average growth 2000-2010; Gini: World Development Indicators, 2009

85The sensitivity analysis examines growth scenarios with the following range in the chosen parameters in 2020: 1.37-1.41 billion people, a per capita income of $3,900-$5,200 PPP, and a Gini coefficient of 0.32-.42. The low GDP growth rate is the average growth rate in the previous two decades, while the high represents the „aspirational‟ growth rate in government projections. See Ministry of Environment and Forests, Govt. of India (2009). The chosen increase/decrease for the Gini coefficient closely matches the actual increase (~.33 to ~.37) from 2000-2007, according to World Bank data. The high population growth rate represents the highest rate between 2000 and 2009. The same gap between the BAU and high was applied to yield the low rate. 86 The mitigation cost is based on McKinsey‟s upper estimate of 20€/ton for India (See McKinsey and Company (2009). Environmental and Energy Sustainability: An Approach for India. Mumbai.). Global income above the poverty threshold has been calculated based on a global Gini coefficient of 0.60 (Sutcliffe, B. (2004). "World Inequality and Globalization." Oxford Review of Economic Policy 20(1): 15-37.). 87 The financial impact of uncertainty does not vary proportionately with changes in the threshold itself. Indeed, the elasticity of poverty to growth and inequality may depend on the chosen threshold. The larger the gap between the exemption threshold and average income (as is the case with the US poverty line), the less elastic is poverty reduction to changes in growth and inequality. 94

The financial impact of varying the exemption threshold is unsurprisingly the most influential. Changing the threshold to $13/day, the US poverty line, doubles the external exemption burden. Among the three economic indicators affecting income distribution about the threshold, the Gini coefficient has the largest individual impact for the chosen ranges. It is interesting that the variation in population growth has a small impact on its own of ~$1billion. That India‟s population growth rate has been on a steady, uninterrupted decline (from 2% in 1991 to 1.4% in 2009) further weakens the importance of population as a risk to increasing external burdens to support the exemption of the poor. Even if this trend reversed, the incremental emissions impact of higher population growth would be benign in the next two decades.

As mentioned, the other two factors in addition to poverty levels that influence the external exemption burden are carbon intensity of the poor‟s income and the cost of mitigation. The baseline scenario incorporates India‟s commitment to voluntarily reduce its carbon intensity by 20 percent by 2020. If instead India‟s carbon intensity remained unchanged from today, the mitigation burden in BAU would increase by $20 billion. Lastly, if mitigation costs were $50/ton instead of the baseline estimate of $35/ton, the mitigation burden in BAU would increase by $23 billion.

The main lesson from this illustration is that the financial impact of uncertainty in internal economic outcomes relative to the baseline is considerable. More importantly, all of these outcomes can be influenced by state policies to various degrees, making them vulnerable to a conflict of interest between developed and developing countries. One has to compare these outcomes to each other with caution: this analysis does not give any indication of how likely any of these deviations from BAU are, or the degree to which they can be influenced by policy. Nevertheless, one can learn from this analysis that for burden-sharing proposals that aim to exempt the poor, rather than poor countries, income distribution deserves equal attention as does average GDP - a notion that has been underemphasized in the climate justice literature.

2.3 Sharing Responsibility for Exempting the Poor Should the cost of exempting the poor be shared equally by all states given the influence of policies in states that benefit from this exemption? One could object to 95 the expectation that all states equally support the poor‟s exemption on the grounds that poor‟s emissions may be 'avoidably high‟ (or, in terms of the previous example, that a country could do „more‟ to be on Path A than on Path B). It seems reasonable then to be concerned about states‟ efforts to reduce poverty and the poor‟s emissions if states receive significant financial support to support their mitigation. The obvious questions are: what does 'avoidable' mean, and against what baseline can states be reasonably held to reduce the poor‟s emissions? This question can be asked of the carbon intensity of the poor‟s consumption and of the extent of poverty in the first place.

Policies that can reduce the poor's carbon emissions profitably seem obvious candidates. Others have already pointed out that countries should pursue “no-regrets” policies – those that reduce emissions and dead-weight loss in the economy (Baer et al 2008). For example, reducing electric distribution losses in rural areas or distributing energy efficient light bulbs can often be profitable to utilities. The challenge here is that hidden costs, market failures or deep-rooted governance problems often mask the feasibility of such policies, even in developed countries.88 In India, for example, reducing distribution losses has been the subject of various reform efforts in the energy sector for decades, with limited and spotty success. Thus, while changes may be important to push, it is unclear how much or where such change is reasonable to demand.

Even less obvious is how to encourage profitable low carbon growth. Here too, concrete mitigation options can be identified that have developmental benefits, such as the promotion of natural gas for future power generation, providing the poor with improved cook stoves, or planning public transportation systems in growing urban centers. Indeed, a vast literature exists for developing countries on this topic, as summarized in the IPCC‟s Fourth Assessment. However, the review emphasizes the lack of sufficient context-specific research in defining alternative pathways, particularly with regard to implementation. In particular, the report highlights the need

88 A classic example is of the putatively 'negative cost' energy efficiency measures that economists often cite but that continue to remain unexploited. See McKinsey Global GHG Abatement Cost Curve, Project Catalyst, 2009. 96 for more research on non-climate polices that affect GHG emissions and sinks.89 There is thus no a priori standard to identify low-carbon policies that would definitively have net positive benefits. Again, while reasonable in intent, the task of operationalizing an obligation on the part of developing countries to „minimize‟ carbon intensity is daunting.

The even more onerous ethical challenge is whether one can define 'avoidably' high poverty levels. From the outside, mitigation burdens that push people into poverty may be seen as the proverbial “straw that broke the camel‟s back”. Going forward, it is tempting to think that states should be held to some fair and objective standard of effort to reduce poverty. But what is a reasonable moral basis for determining the appropriate level of commitment towards poverty alleviation?

In principle, it may be possible to identify policy reforms that would alleviate poverty. The kerosene subsidy in rural areas, as discussed in Chapter 2, is one example of this. Three quarters of the kerosene subsidy value does not reach the intended beneficiaries. If the subsidy were redesigned and restricted to urban areas, the benefits to the poorest households would increase, which would contribute marginally to reducing poverty levels. This sort of policy reform doesn‟t require rooting out the entrenched interests that control the delivery of subsidized kerosene. One may thus argue that the state would be negligent in not pursuing this re-design. However, what is not known is how the recovered fiscal revenues would be otherwise used, what other institutional constraints reforms may face, and what eventual distributional impacts would result. To stake financial commitments on the expectation of policy reform then would be equivalent to placing bets on counterfactuals that foreign entities may not have the knowledge to justify.

Given the numerous context-specific determinants and complex nature of poverty, and the difficulty in predicting policy outcomes and feasibility, recommendations for policy reform would run a high risk of failure themselves, or have to be framed at a

89 IPCC, Sustainable Development, Working Group III Contribution to the Fourth Assessment Report: 12. 97 very general level, neither of which would necessarily be an improvement over the status quo. Besides, if causation is indeed the basis for determining states‟ contribution to the cost of exemption, one runs into intractable questions about the root causes of poverty and the relative contribution of historical, global and local factors.

In summary, holding states to an emissions baseline for the poor would be at best hard to justify or enforce. At worst, it could rob state governments of the autonomy to choose a development path that they are in the best position to choose. In principle, no state should enjoy carte blanche sovereign powers over policies that increase carbon emissions without providing any development benefit.90 Climate change irreversibly changes how sovereignty needs to be viewed henceforth, as indicated by the UNFCCC preamble.91 The challenge lies, however, in operationalizing this principle in a manner that doesn‟t place unreasonable expectations of institutional change on developing countries. It seems difficult to have reasonable moral guidelines for distinguishing between negligence and legitimate constraints to reducing the poor‟s emissions.

A vital lesson from this discussion is that even though in principle the global community‟s limited duties to avoid imposing mitigation burdens on the poor can be separated clearly from states‟ broader duties towards poverty alleviation, in practice, quantifying and implementing the former seems inextricably tied to the latter. The next section discusses the conflicting incentives that these policy uncertainties can create for countries that negotiate climate mitigation agreements with the goal of exempting the poor.

90 This point has been made by Caney (2010) as well. 91 Preamble of UNFCCC: „Recalling also that States have, in accordance with the Charter of the United Nations and the principles of international law, the sovereign right to exploit their own resources pursuant to their own environmental and developmental policies, and the responsibility to ensure that activities within their jurisdiction or control do not cause damage to the environment of other States or areas beyond the limits of national jurisdiction [italics added]‟. 98

2.4 Conflicting Incentives in Designing Exemptions The dependence of the external exemption burden on the income distribution around a poverty threshold creates conflicting incentives for states. Income growth targeted to the abject poor doesn't reduce the external exemption burden until and unless it pulls people out of poverty altogether. For example, providing electricity access to remote villages may catalyze their income growth, but they may still be vulnerable to price increases and therefore continue to require an exemption from mitigation costs. At the other end of the income distribution, growth that accrues to wealthy classes increases the states' mitigation obligation, but not the external exemption burden. However, growth that pulls people out of poverty shifts mitigation burdens from the global community to the state.

This (considered in isolation) implies that the state has an incentive to direct income growth away from poverty alleviation (with respect to the threshold). However, it is probably far-fetched to suggest that a mitigation agreement would alter their priorities with regard to poverty alleviation given the development pressures and resource constraints developing countries face.

The same can‟t be said of the developed countries, if conditioning their support to developing countries on certain policy outcomes were inexpensive relative to the stakes in the external exemption burden. This may motivate developed countries to base their financial commitments to exempting the poor on very aggressive estimates of future poverty alleviation, and/or commit additional support conditional on policy reforms. This has practical implications. Developed countries may indeed prefer to support „trickle-down‟ development policies, which justify the growth of the middle classes as a vehicle towards, or supporting, poverty alleviation. Indeed, some evidence supports this argument (Ravallion 2009). In India, growth has arguably proceeded along this model in the last twenty years, but with questionable gains to the poorest.92

92 While there is empirical evidence showing correlation across countries between GDP and poverty reduction, there is significant variance in the extent that growth has benefited the poor at any given growth rate. See Ravallion, M., and Gaurav Datt (2002). "Why has economic growth been more pro- poor in some states of India than others?" Journal of Development Economics 68: 381-400., Banerjee, 99

But would rich countries care enough to meddle with internal policies of developing economies?93 The previous sections illustrate that the financial stake in policy uncertainty may be comparable in magnitude to states‟ entire claims for exempting the poor. However, if these claims are small relative to rich countries‟ overall mitigation obligations, the likelihood is low. This depends in part on how mitigation burdens are shared among the non-poor. If, for example, states‟ mitigation obligations were proportional to their historical emissions, the adjustment for exempting the poor may be small. Müller et al indeed calculate that if states‟ mitigation burdens are calculated based on their causal contribution to climate change from 1890, the adjustment in burdens to exempt those earning below $1/day is almost negligible (Muller and Niklas Hohne 2009). However, if mitigation responsibility is calculated based on ability to pay, and the threshold used is $20/day, as is the case with GDR, exempting the poor has a significant impact on states‟ allocations. If an exemption for the poor were a subject of international negotiation, the threshold level would be another obvious basis for disagreement. It is worth noting, in any case, that international climate negotiations have already faced political controversy over the mere monitoring of emissions. The prospect of disagreement on the choice of emissions trajectories and exemption levels would likely complicate negotiations even further.

3 Subnational (Mis) Allocation of Mitigation Burdens So far the discussion has focused on the influence of different future income distributions in developing countries on burden sharing among developed and developing countries. But for a given distribution of burdens to exempt the poor in a developing country, extending their benefits to the poor at a subnational level presents a whole new set of challenges. That is, what if the implied 'subsidy' intended for the poor's emissions get diverted to the rich's emissions reductions, and the poor face higher energy prices anyway? Does the global community have an obligation to

A., and Thomas Piketty (2005). "Top Indian Incomes, 1922–2000." World Bank Economic Review 19(1): 1-20. 93 It would be useful to compare the incentives and structure of climate mitigation agreements to other international agreements where conditionality has been imposed and resisted. That discussion is beyond the scope of this paper. 100 ensure that justice obtains at the individual level, or does their obligation end at the border once they provide a fair share of the cost of the poor's mitigation?94

Some clarifications of the circumstances of interest here are worth mentioning. First, the misallocation of benefits can arise from institutional limitations, rather than the more obvious concerns about the potential misuse of funds, if any, for purposes other than mitigation – the MRV95 provisions in the Bali Action Plan demonstrate that both Annex I and non-Annex I parties have agreed to the importance of linking assistance to mitigation. Second, this issue is only of concern (as was the case in the previous section) in states that have substantial rich populations, and therefore mitigation commitments of their own. Otherwise, if the only mitigation that states implemented was through external funds, then the issue of misallocation would not necessarily arise – energy prices would not have to increase at all. Lastly, this concern, in principle, also applies to “resource-sharing” (per capita emissions) views of allocating mitigation costs. If states receive an allocation to global carbon „space‟ that is enjoyed by a minority at the expense of the poor, not only would justice not be served, but this minority would free ride while in other states people with similar emission levels may have to mitigate. Is it a global concern then whether the poor enjoy the benefits of their per capita entitlement on the basis of which a state receives an exemption from mitigation?

In the first subsection, the institutional conditions that give rise to this misallocation problem are discussed. Then, the ethical issue of whether the external exemption burden should extend to individuals, rather than states, is discussed, with some suggestions for its resolution.

94 This issue has been raised before by some scholars. Caney and Singer argue, in principle, that the duty to avoid harm extends to individuals. Posner cites this as one argument against distributive justice considerations in climate mitigation (Posner, E. A., Cass. R. Sunstein (2007). Climate Change Justice. John M. Olin Law and Economics Working Paper Series. Chicago: August 2007.) 95 “Measurable, Reportable and Verifiable” 101

3.1 Intranational Misallocation of Mitigation Burdens The poor may face mitigation costs despite an exemption granted to a state on their behalf due to a combination of three factors: the diffused nature of energy consumption in an economy; the limited capacity for redistributive mechanisms in developing economies; and corruption.

Diffused Energy Production The typical industrial organization of energy systems in an economy is such that most energy services are produced at centralized locations, such as power plants and industries, and then distributed to diffused and numerous points of consumption through common markets and delivery systems. As shown in Chapter 3, higher energy prices affect households indirectly through the increased production costs of goods and services more than direct consumption of energy for homes and transportation.

Limited Capacity for Redistribution In order to insulate the poor from mitigation costs, governments need to be able to identify who they are, then either avoid taxing them, or compensate them. Countries have limited mechanisms to target mitigation to the wealthy. They can tax „luxury goods‟, such as automobiles and air conditioners (though even these may be essential in certain geographic and economic contexts). Broader mechanisms to identify and target the poor are exceedingly weak. Developing countries with significant poverty lack a broad-based income tax system. Further, in many of these countries a significant share of economic activity takes places in „informal markets‟, which broadly speaking are outside the control of policy. As a result, governments rely on a number of alternative redistributive mechanisms to target the poor – welfare programs, administrative cash transfers, and price-based subsidies – that have varying degrees of success in different regions. These two factors are discussed below in more detail.

Narrow Tax Base: Broad-based income tax systems, as are common in industrialized economies, serve as useful mechanisms to target households for redistribution. This is particularly convenient for implementing equitable climate policy, since policymakers can allow market prices of goods and services to reflect the full cost of carbon, and compensate select groups through income tax credits. 102

In developing countries, however, the tax base does not extend to a significant share of the population. In India, 2-3 percent of the population pays taxes. In China, due to tax reforms in the last decade, the tax base only recently increased from 0.1 percent in 1986 to 20 percent in 2008 (Piketty 2009). The reason is that the bulk of the population doesn‟t earn enough to be taxable, but also because of weak enforcement that results in tax evasion (Gordon 2005).

Informal Economy: A second related institutional limitation is that a large share of economic activity takes place in the informal (cash) economy. As Table 11 shows, the informal economy comprises 13-34 percent of GDP in developing countries. These figures are also likely to be significantly understated, given the inability to track these activities. What is not shown is that an even larger share of the labor force in the country, into which most of the poor fall, earns their income in the informal economy (for e.g., over 80 percent in India). This makes it difficult to know peoples‟ income, let alone target them based on their income. Further, since prices of goods in these markets are largely outside the control of policy, the poor‟s reliance on them limits the efficacy of subsidy policies to target them. Finally, even in the absence of rent- seeking, subsidies don‟t reach the poor who have limited access to delivery points of subsidized goods, because they are too far or too expensive to access, such as in sub- Saharan African countries. Indeed, due to varying access conditions, among other differences, the effectiveness of consumption subsidies varies widely across countries (Komives 2005).

Table 11: Informal Economies in Developing Countries

Countries grouped/ Tax Revenue Informal Economy ordered by GDP. (% of GDP, 1990- (% of GDP, 2001)1 1990/91) 1 Quartile 1 26.6 13.5 Quartile 2 21.4 26.9 Quartile 3 17.5 34.2 Quartile 4 13.3 28.8 Source: Gordon and Li, 2005 1. Average across countries in each Quartile

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Institutional Corruption The third factor that makes a misallocation of mitigation burdens likely is corruption. This is well demonstrated in the administration of energy subsidies discussed in Chapter 2.

Mistargeted Subsidies: Many developing economies subsidize „subsistence consumption‟ – such as grains and household fuels - as an alternative mechanism of redistribution. However, these subsidized products are often delivered to their beneficiaries by bureaucracies that, in the absence of effective detection, face strong incentives to extract rents and divert these subsidized products to more lucrative buyers. For example, Chapter 2 highlights the diversion of a third of subsidized kerosene by corrupt suppliers. Further, most countries have tariff structures for providing energy services that allow for some income-based price discrimination using proxies for income. However, as described in detail in Chapter 3, political constraints cause policymakers to underutilize these mechanisms. In the case of electricity tariffs, regulators face pressure to increase all households‟ prices towards average cost and reduce industrial rates, when cross-subsidizing low-income tariffs with higher industrial and high-income residential rates would be both equitable and efficient.

These institutional conditions make it likely that mitigation costs may be poorly targeted, so that the poor end up bearing much of the mitigation costs in the economy, if mitigation efforts are deep enough.

3.2 Duty of Exemption Extends to States or to Individuals? Given the above institutional failures, is it sufficient for participating countries to provide states with the means to avoid harm to their poor, or should parties to an agreement also have a duty to ensure that the poor receive the benefits of an exemption? What principles should apply in resolving this? Prima facie, it seems that since the interests that the external exemption burden seeks to protect are fundamentally those of the individual, satisfying this duty must require that benefits reach the concerned groups. However, if doing so involves significant cost, or requires violating states‟ sovereignty, the extent of this duty is not obvious. 104

Three arguments may justify ending the duty at the border: first, that intervention may be excessively burdensome; second, that respect for sovereignty trumps the cause of ensuring justice obtains at the individual level; and third, that the global community has no causal role in the failure to deliver the benefits to individuals, so no such duties arise. In resolving the first two concerns, a lot seems to ride on the nature of possible remedies. If mitigation actions can be identified a priori that target the poor and are agreeable to host countries (e.g., improved biomass cook stoves programs, solar lanterns), then parties to an agreement only have to ensure that they design agreements with that explicit goal. But does their obligation to do so depend on their causal role?

A view of the potential misallocation of mitigation burdens as a form of „institutional harm‟ – where the institution is the international climate mitigation agreement - offers two additional principles to address the causal contribution argument.96 First, purely on a consequential basis, an institutional scheme is unjust if it violates human rights in a manner that is reasonably avoided and foreseeable, and when a more just alternative scheme is possible. An institution (the mitigation agreement) may be implicated even if it causes harm through the actions of another institution (the developing country government). By this standard, if remedies exist, under which the likelihood of the poor benefiting from mitigation assistance is far greater, it seems just to pursue these remedies.

However, different actors within institutions can have different roles and duties to extend benefits to the poor. These duties depend on several factors, including the extent actors‟ benefit from the wrongdoing in question. One can apply this argument to the difference in duties between states. The rich citizens of a developing country benefit entirely from the harm caused to the poor if mitigation support subsidizes their own energy needs and not those of the poor. Further, rich citizens share a social contract with the poor, and therefore have greater agency than other states to influence future patterns of growth and redistributive policies. They should therefore have a greater degree of responsibility than other states‟ citizens. Thus, even though all states

96 Pogge, T. (2005). "Severe Poverty as a Violation of Negative Duties." Ethics & International Affairs 19(1): 55-83. 105 share equally in the financial obligation to support the interests of the poor, they do not necessarily share in the duty to implement mechanisms to reach this compensation to the poor. These must lie with the benefiting states.

How should these various principles be applied to the case at hand? One compromise could be that states collectively pursue the more „burdensome‟ options that target the poor (such as, distributed electric systems). The relative responsibility of different agents within an institution shouldn‟t alter the choice of institutional scheme that serves justice better. But the additional costs, and duty, to implement them must be borne by local actors. More practically, technology transfer and other forms of mitigation assistance that form part of an agreement should include means and technologies targeted to the poor. The responsibility to design and implement such systems and their costs, however, should be borne by local governments.

3.3 Incentives to Enforce Exemption While such a compromise may seem morally persuasive, from a practical standpoint it may be politically infeasible. Unlike the previous case where parties have a financial interest in states‟ influence over the external exemption burden, here parties have no financial stake in ensuring that justice obtains at the individual level. The only potential incentive they would have is that a misallocation of an exemption could be perceived as encouraging unnecessary transfers from developed countries to the elite in recipient states. On the other hand, the threat of interference in benefiting states‟ sovereign affairs could jeopardize their participation in a climate agreement. Developed countries may be loath to take this risk, if a poorly targeted mitigation obligation is considered a price worth paying for securing a mitigation commitment from a fast-growing developing country.

4 Concluding Thoughts: Implications for Climate Policy The participation of the BASIC countries and other large emerging economies in climate agreements may well depend on the ability of parties to carve out an exemption for poor populations within these countries. This study shows that implementing such an exemption would likely require parties to confront these states‟

106 internal policies towards growth, poverty alleviation and income distribution. This is because these policies affect how much an exemption would cost, and who within benefiting states would actually benefit from this exemption. The challenges this concerns raise are practical: how should an exemption be designed and implemented so as to get agreement among parties on its terms and also ensure that its objectives are achieved? I have argued that from an ethical standpoint parties to an agreement ought to design agreements in a manner that enhances the poor‟s ability to receive the benefits of an exemption. Beyond this, parties should respect states‟ sovereignty with respect to defining their future emissions and eligibility for an exemption. However, parties‟ incentives to enter into such an agreement run in opposition to these ethical demands. Net creditors of an exemption (developed countries) have little incentive to meddle with how to better target the benefits of an exemption in benefiting states, but have stronger incentives to condition their participation on those states‟ domestic policies or to actually influence these policies.

4.1 Designing Acceptable Terms for an Exemption To get agreement on the terms of an exemption, crediting parties need some assurance of benefiting states‟ efforts towards reducing the poor‟s emissions. At the same time, benefiting states would be wary of how intrusive these assurances may be. This could raise sovereignty conflicts of the kind already seen with less intrusive actions such as emissions monitoring.

One compromise may be to seek comparability in effort between benefiting states and the external crediting states. This approach is already enshrined in the Bali Action Plan for developed countries. Lessons can be drawn from these experiences for the purpose at hand. For example, one criterion could be to require states to 'match' the effective income relief provided through external support with investments towards poverty alleviation. That is, if the mitigation obligation led to a resource transfer (or avoided mitigation cost) of $10 million every year for five years for the poor, it would be reasonable to expect that at least $10 million of the annual GDP growth were invested in services or livelihoods that raised some of the poor above the exemption

107 threshold. How to make these investments feasible and yield results would need to be worked out, but it could serve as starting point for negotiations.

Another measure of comparability could be best practices among developing countries that receive exemptions. For instance, the exemption could be tied to an average change in some the poor‟s emissions, or some measure of development such as the HDI, over a chosen period of time. Such a standard could be adjusted for heterogeneity in national circumstances, but it would allow for some objectivity in holding states accountable to the poor‟s emissions intensity.

4.2 Enforcing an Exemption Successfully targeting an exemption within a benefiting state presents a different set of challenges, related more to the intrusiveness of the measures required to enforce an exemption than to a conflict of interest about the intended outcome. However, the potential for conflict arises over how to monitor an exemption‟s implementation and whether conditions should be placed to ensure that the poor benefit from an exemption.

A relatively easy solution, but with limited potential, is to provide an exemption in the form of low carbon technologies rather than to reduce states‟ mitigation obligations to accommodate an exemption. Certain types of technologies can be targeted directly to the poor. Some of the properties of technologies that make them suitable for targeting and less prone to diversion are how portable these technologies are, and whether their use is exclusive to the needs of the poor. For instance, distributed power generation systems that cater to villages or small towns are good candidates because they are relatively expensive to relocate once established, due to the costs of siting, permitting and construction. End use technologies, such as solar lanterns, would also be good candidates because they have value mostly in remote regions that are not connected to the electric grid. While many non-poor do live in remote regions, their proportion is lower relative to regions with grid access. End-use technology transfers are particularly important among the poor who have energy access and are served by common energy delivery systems and institutions, since supply side technologies

108 cannot be easily targeted to particular consumers. Aggregate indicators of penetration rates in different regions and household types can be monitored to track enforcement.

However, the scale of mitigation through decentralized technology options is limited by demand growth in remote areas and by the potential for demand reduction through energy efficient technologies on the demand side. For a high-growth economy, mitigation options for centralized electric supply would be unavoidable, since the bulk of new electric capacity expansion would take place on the grid. For these grid- connected poor, regulators‟ pricing policies could be monitored. As shown in Chapter 3, increases in industrial rates over residential rates would have a relatively lower budget impact on poor households. With regard to residential rates, relative rate increases across income groups over a specific time period could be another metric to monitor how mitigation costs were being distributed. If macroeconomic factors that are unrelated to mitigation costs were to drive electricity prices, these ought to affect all households equally, and therefore would be controlled for, albeit crudely, in a comparative measure.

With regard to household cooking needs, any biomass-based initiatives would largely serve poor households and have limited potential for diversion to higher income households. In urban areas, improved kerosene stoves targeted to slums would have a demand mostly among poor households, as shown in Chapter 2.

These are preliminary thoughts for implementing an exemption in the electricity sector and in households, all of which require further elaboration to determine robust ways of relating mitigation policies on the one hand to household budget impacts on the other. Mitigation policies undertaken in other sectors such as transportation or industry may be harder, if not impossible, to trace to households. In these cases, other proxies of targeting may be necessary, such as the geographic location of mitigation actions, such as public transportation in towns, or small-scale industry that support low income livelihoods.

This preliminary observations show that enforcing an exemption is a complex exercise, involving significant contextual knowledge. International agreements that are

109 committed to protecting individuals will have to manage sovereignty concerns in determining appropriate implementation and monitoring strategies.

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CHAPTER 5 – CONCLUSIONS AND FUTURE RESEARCH

In this dissertation, I have examined the implications of defining and implementing mitigation obligations for subpopulations above a given income threshold within developing countries. The first two studies examine the distributional impacts around such a threshold of implementing climate mitigation policies in India, focusing on the influence of institutions that design and implement energy policies. The third study examines the moral and practical challenges that arise in including such an exemption in international climate policy. Below I summarize the main findings and broader implications of the dissertation, and suggest future research directions.

1 Distributional Impacts of Energy and Climate Policy in India This dissertation is the first formal study of the income distributional impacts of kerosene subsidies (Chapter 2) and future climate mitigation policies in India (Chapter 3). Both studies fill an important gap in literature on the benefits-side of energy subsidies. Both studies also highlight the importance of quantifying the influence of governing institutions that implement energy policies. The specific lessons that emerge from the two studies are summarized below.

1.1 Climate Mitigation in the Electricity Sector The income distributional impacts of mitigation through low carbon electric supply investments vary widely with regulators‟ pricing policies. This study shows that lower income groups would be better off if mitigation costs are passed on through industrial rate increases rather than residential rate increases. Regulators have the leverage in rate-setting to shield low income groups from mitigations cost increases of up to 35 percent. The trade-off, however, is that high income households would face welfare losses equivalent to 4-16 percent of their household expenditure. The wide range in this trade-off is driven by the extent to which regulators can recover incremental costs from industrial consumers. Political pressure to keep industrial prices low may prevent

111 regulators from exercising this leverage even though aggregate welfare losses from mitigation may be lower with higher industrial rates.

This study offers two implications for evolving climate policy in India. First, the study reveals an unrecognized synergy between carbon mitigation and human development. Climate mitigation provides a justification for increasing electricity prices as a means of reallocating scarce electric supply, when institutional constraints prevent adequate investment in supply expansion. In rural areas with supply interruptions of 8-10 hours, households suffer as much or greater welfare losses from unmet demand as they would from satisfying that demand at higher average rates. If regulators indeed improved supply to these areas with higher prices, the rural poor would benefit from mitigation. Furthermore, their welfare gains from improved supply offset the welfare losses faced by other households from higher prices even without considering other benefits to rural households related to livelihoods, education, and quality of life.

More broadly, Indian policymakers need to give attention to the distributional impacts of low carbon policies. The government made its pledge in Copenhagen with the hope that the recent trend of declining carbon intensity of the economy would continue. This study shows that if this is not the case, and meeting the target requires supply- side investments in low carbon technologies, low income groups can face non-trivial consumption shocks from the resulting price increases. Furthermore, these impacts may be avoidable and exaggerated by governance failures. So far India‟s focus in defining its Nationally Appropriate Mitigation Actions (NAMA) under the Bali Action Plan has been to identify actions that have net developmental benefits („no regrets‟). As India faces increasing political pressure to undertake more mitigation, and more of the population stands to face energy price increases, the criteria for appropriateness ought to expand to include targeting effectiveness.

1.1.1 Energy Models and Non-market Institutions The methodological contribution of this study is to simulate aspects of governance in the Indian electricity sector. The simulation model used in Chapter 3 incorporates regulatory constraints in price-setting, and utilities‟ supply rationing behavior. The distributional impacts of interest here depend heavily on these institutional influences.

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Furthermore, the economic impacts of particular institutional actions or policies have been quantified. This is an important direction of research for energy modeling in developing countries, where distributional outcomes and institutional reform are of interest to policymakers.

1.1.2 Further Research The study has focused on accurately assessing consumption shocks around a baseline of income distribution as a result of electricity price increases. In the longer term, growth effects from sustained price increases can have additional distributional impacts. Industries may shift investments away from electricity-intensive technologies, causing changes in wages and profits in upstream industries. This requires a disaggregated view of industry to capture heterogeneity both across and within industry sectors in India. Data for such a disaggregation are not easily available, and may require additional primary research. Further, due to the large role of the public sector in the provision of social services and infrastructure, further research is required to understand the distributional effects of changes in public service expansion.

1.2 Kerosene Subsidies The Indian government has been advised by an expert committee to phase out kerosene subsidies for households due to their high fiscal costs and substantial diversions to other sectors. This study suggests such action may be premature and have adverse distributional impacts in urban areas. Kerosene subsidies offer substantial direct benefits to the urban poor, up to 5-10 percent of households‟ budgets among those who cook primarily with kerosene.

The study distinguishes between the relative contribution of design failures and implementation failures. Half the subsidy value is lost largely due to design failures. Restricting the subsidy to urban areas could substantially reduce these losses by restricting the upstream supply of subsidized kerosene that gets diverted by rent- seekers. Policing the numerous points of diversion and rent extraction along the supply chain, on the other hand, yields fewer benefits and is likely to be burdensome.

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An important implication of this study is that policymakers need to evaluate the subsidy policy more formally as an instrument of income redistribution against other „second-best‟ redistributive mechanisms. This study puts forth one metric for such a comparison – fiscal cost per unit of delivered value – that should be used, among other considerations, in evaluating alternatives. The institutional feasibility and cost of either reforming or eliminating the subsidy, however, is an important element of such a comparison that this study has not investigated.

1.2.1 Further Research Subsidized kerosene that is diverted to transportation and construction sectors lowers those sectors‟ input prices. This also can have distributional impacts, most likely as profits in benefiting industries. Because the bulk of diversions are controlled by organized crime in Maharashtra, and in many other states, the benefits are likely to be concentrated among few. Nevertheless, a complete characterization of the distributional benefits of the kerosene subsidy would include these effects. These data are hard to access for obvious reasons.

2 Implementing Exemptions for the Poor in International Climate Mitigation Agreements This dissertation has confronted the problem of operationalizing a human-rights view of sharing the burdens of climate change mitigation, which avers that poor people below a certain threshold of living standards should remain insulated from society‟s costs of shifting to a low carbon economy. Many proponents of this view recommend exempting countries from mitigation based on their average GDP or emissions. However, for large emerging economies such as China, India, Brazil and South Africa, that have comparable emissions from both rich and poor populations, such a simplification may be ethically unreasonable. If they undertake mitigation, the poor may be exposed to unacceptable burdens, and if they get exempt, the rich get a free ride.

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This study provides insights on the claims and duties that states who receive an exemption for their poor ought to have, as well as on the challenges in getting agreement among parties to an agreement on the terms of such an exemption.

2.1 State Claims and Obligations Burden-sharing proposals that allocate mitigation responsibility to subpopulations within states are insufficiently developed to be implemented in international agreements between states. This study raises two issues:

(1) Burden-sharing proposals that include an exemption for the poor do not address whether the poor will actually receive the benefits of an exemption when agreements are implemented within states.

To prevent such a misallocation of burdens, the international institution that implements a climate mitigation regime should have some obligation to enforce an exemption for the poor. However, participating states could have different duties to enforce this exemption. One way to share duties is that the international institution should be responsible for designing an exemption in a manner that intended beneficiaries get their due (for e.g., by providing low carbon technologies for the poor instead of reducing a state‟s mitigation obligation, and monitoring compliance). Benefiting states should bear the additional costs of implementing these mitigation efforts, since they influence these costs. These solution ideas need further exploration and development in particular country contexts.

(2) Burden-sharing proposals that define exemptions for the poor do not question the basis for an exemption. Since states that receive exemptions exercise sovereign control over the number of poor and their emissions, one can question whether they should be held accountable for the magnitude of these emissions over time.

The resolution of this question reduces to a narrower question: whether it is possible to objectively differentiate states‟ negligence in reducing the poor‟s emissions from legitimate institutional constraints. Holding states accountable may be justified in the former case, but not as easily in the latter. While in specific instances such a

115 distinction may be possible, to define and quantify an objective moral standard of negligence for a country as a whole seems unreasonable.

If benefiting states are held to an unattainable standard, either the poor would be exposed to excess mitigation costs or the non-poor in these states would risk bearing a disproportionately high mitigation burden in comparison to their counterparts in other countries. Thus, states should have the benefit of doubt over the development and fuel choices for the poor. For foreign entities to define norms for individual states‟ efforts would be unreasonable.

2.1.1 Further Research Human rights-based views of burden-sharing need to address other empirical conditions in implementing principles of distributive justice. In addition to sovereignty concerns, commensurability in living standards across countries also poses challenges. For example, for climate regimes predicated on the ability to pay principle as a basis for distribution, should capacity include just income or wealth (which may not contribute to emissions)? How should financial wealth be compared against other kinds of wealth (such as land, or cattle)?

The issues raised here apply to broader issues in global distributive justice as well. Cosmopolitan theorists who advocate for a more positive duty on the part of rich countries towards redressing global income inequality also face this interdependence between sovereign and international duties. Scholars have confronted both the issues raised in this dissertation, but there is a need for further discussion of how these duties can coexist and yet be disentangled.

2.2 Getting Agreement on Exemptions for the Poor Since the costs of accommodating an exemption in climate agreements depend on benefiting states‟ sovereign policies, parties are likely to clash over the terms of an exemption. With regards to enforcement, where accurate targeting of an exemption is unlikely without concerted efforts to enforce it, participating states may have no incentive to make such efforts. Crediting states may provide an exemption to states only to secure their participation in a mitigation agreement, and therefore may not be

116 concerned who bears mitigation so long as the national mitigation target is met. States that receive an exemption, as discussed, may also not make concerted efforts to surmount their targeting failures if special interests benefit from the exemption.

Thus, these duties of enforcement would need to be institutionalized in climate agreements by the UN or other independent governing body. In practical terms, the best way to ensure that the poor receive the benefit of an exemption is to provide direct mitigation support in the form of technologies rather than to adjust states‟ mitigation obligations downward to account for an exemption. There are several technological options that cater directly to the needs of the poor, such as improved biomass cook stoves, decentralized solar lighting or distributed power generation.

With regard to the terms of an exemption, benefiting and crediting states have a financial interest in influencing the chosen baseline of the poor‟s future emissions. The stakes in the variability of such a baseline based on different rates and distributions of income growth would be in the range of $1 billion a year for the case of India, if an exemption were granted for those earning under $9 per day (which is the highest developing country poverty line).

States that would be net creditors would have an incentive to negotiate low exemption thresholds and base their support on aggressive estimates of poverty alleviation in benefiting states. Given the ample evidence of inequitable development policies in developing countries, developed countries would be hard pressed to unconditionally accept developing countries‟ own projections. But benefiting states would be wary of conditions placed on their own sovereign policies.

One compromise may be to seek comparability in efforts between benefiting states and crediting states. This approach is already enshrined in the Bali Action Plan for developed countries. Another measure of comparability could be best practices among developing countries that receive exemptions. For instance, the exemption could be tied to an average change in the poor‟s emissions, or some measure of development such as the HDI, over a chosen period of time.

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2.2.1 Further Research In addition the solutions discussed above, further research can identify ways to link climate agreements with other international agreements that include conditionality and/or protection for particular subgroups to understand how to balance the interests of both benefiting and crediting states.

This study has not focused on the challenge of selecting an exemption threshold. Though a reasonable threshold ought to provide for some measure of living standards beyond subsistence, quantitative measures are lacking, particularly in different contexts. The vast discrepancies in poverty lines across the globe make for considerable disagreement in defining an exemption threshold. Most countries define poverty lines as a relative measure of deprivation, which manifests in the pattern that poorer countries tend to have lower poverty lines. Developing countries would find themselves in an awkward position if they argued for higher poverty lines in climate agreements than their own domestic ones, even though higher ones would be justified.

The highest developing country poverty line ($9/day) may be a good place to start. Further empirical work in developing countries can illustrate what living conditions this represents in different contexts.

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Appendix A - Maharashtra Kerosene Quota Allocation

Source: Maharashtra Food, Civil Supplies and Consumer Protection Department. (http://www.maharashtra.gov.in/english/food/schemesKerosene.php)

Methodology for Determining Household Quotas from NSSO Data Households‟ entitlements were calculated based on fuel use data in the NSSO0405. Households that did not use LPG were identified by the absence of LPG consumption in the survey. The NSSO0405 does not indicate how many cylinders households own. Households with two LPG cylinders were assumed to be those households that did not have use any backup fuel. This is a safe assumption, since households can‟t predict when their cylinders will run out and usually cannot rely on timely delivery of replacements. All other households use LPG and likely have one cylinder. Data on household size and location were used to determine the final kerosene entitlement (as defined above).

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Appendix B – Detailed Results and Data Tables (Chapter 3)

Detailed Results

Income Groups -> AB Region CD Region EF Region Reference Parameters 1 2 3 4 5 6 1 2 3 4 5 1 2 3 4 Average Household Monthly Expenditure 2,000 4,000 6,000 8,000 10,000 50,000 1,500 3,000 4,200 7,000 8,500 1,500 3,000 4,200 7,000 Electricity Budget (Direct Consumption) 9.5% 8.5% 7.5% 5.9% 5.0% 3.8% 9.5% 8.5% 7.5% 6.0% 5.0% 9.5% 8.5% 7.5% 6.0% Population (Millions) 9.4 7.2 5.9 4.0 1.6 1.0 10.1 7.9 3.0 0.8 0.2 6.3 3.5 2.6 1.2 Welfare Impacts (Rs/month) Baseline Scenarios 1a. No Rationing (Idealized) ------1b. Economic Rationing (Status Quo) (2.2) (3.7) (4.7) (5.0) (0.0) (0.0) (19.2) (32.6) (38.5) (20.3) (0.0) (70.0) (120.8) (79.4) (52.5) 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 1.3% 1.1% 0.9% 0.3% 0.0% 4.7% 4.0% 1.9% 0.8% 1c. Optimal Prices - Eco Efficiency Objective 110.4 185.5 140.7 98.3 58.5 (647.2) 59.3 122.0 139.7 113.3 97.1 9.3 49.5 110.6 85.5 5.5% 4.6% 2.3% 1.2% 0.6% -1.3% 4.0% 4.1% 3.3% 1.6% 1.1% 0.6% 1.6% 2.6% 1.2% Low Carbon Scenarios (Rationing Not Required) 2a. Economic Efficiency (EE) Objective (123.1) (236.5) (314.3) (373.8) (437.6) (1,676.5) (88.2) (175.3) (228.7) (334.6) (380.3) (88.2) (175.3) (228.7) (334.6) 6.2% 5.9% 5.2% 4.7% 4.4% 3.4% 5.9% 5.8% 5.4% 4.8% 4.5% 5.9% 5.8% 5.4% 4.8% 2b. Equity Objective 2.5 (116.3) (654.9) (942.2) (1,105.2) (8,369.9) (1.9) (11.3) (37.2) (603.5) (652.2) (1.9) (11.3) (37.2) (603.5) -0.1% 2.9% 10.9% 11.8% 11.1% 16.7% 0.1% 0.4% 0.9% 8.6% 7.7% 0.1% 0.4% 0.9% 8.6% 2c. Equity with Cost of Supply Constraint (104) (221) (413) (520) (587) (3,462) (74) (146) (188) (397) (422) (74) (146) (188) (397) 5.2% 5.5% 6.9% 6.5% 5.9% 6.9% 4.9% 4.9% 4.5% 5.7% 5.0% 4.9% 4.9% 4.5% 5.7% Sensitivities - Industrial Elasticity (Relative to 2a, (-0.5)) 3a. Low Industrial Elasticity (-0.3) 72 78 (139) (277) (388) (3,546) 45 82 75 (146) (210) 45 82 75 (146) -3.6% -1.9% 2.3% 3.5% 3.9% 7.1% -3.0% -2.7% -1.8% 2.1% 2.5% -3.0% -2.7% -1.8% 2.1% 3b. High Industrial Elasticity (-0.75) (183) (337) (400) (438) (472) (1,192) (129) (251) (315) (409) (433) (129) (251) (315) (457)

Sensitivities - Energy Efficiency 3c. Economic Efficiency Scenario (85.66) (169.22) (230.57) (360.80) (423.04) (1,633.40) (61.08) (125.35) (167.68) (321.60) (367.09) (61.08) (125.35) (167.68) (321.60) 4.3% 4.2% 3.8% 4.5% 4.2% 3.3% 4.1% 4.2% 4.0% 4.6% 4.3% 4.1% 4.2% 4.0% 4.6% 3d. Equity Scenario 2.41 (107.70) (548.48) (891.66) (1,028.03) (7,162.39) (2.00) (11.39) (37.35) (583.23) (627.26) (2.00) (11.39) (37.35) (583.23) -0.1% 2.7% 9.1% 11.1% 10.3% 14.3% 0.1% 0.4% 0.9% 8.3% 7.4% 0.1% 0.4% 0.9% 8.3%

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Table B-1: Household Parameters

Avg Typical Reliability Income Population Average Benchmark Elec Outage Ration

Region Region Group (millions) HH Size Elasticity Budget θs Hrs/Day Share Q Urban AB 1 9.4 5.7 -0.28 9.5% 1.5 0.05 Urban AB 2 7.2 5.2 -0.27 8.5% 1.5 0.05 Urban AB 3 5.9 4.7 -0.26 7.5% 1.5 0.05 Urban AB 4 4.0 4.2 -0.25 5.9% 1.5 0.05 Urban AB 5 1.6 3.7 -0.24 5.0% 1.5 0 Urban AB 6 1.0 3.2 -0.23 3.8% 4.5 0 Urb/Rur CD 1 10.1 5.7 -0.28 9.5% 4.5 0.15 Urb/Rur CD 2 7.9 5.2 -0.27 8.5% 4.5 0.15 Urb/Rur CD 3 3.0 4.7 -0.26 7.5% 4.5 0.15 Urb/Rur CD 4 0.8 4.2 -0.25 6.0% 4.5 0.1 Urb/Rur CD 5 0.2 3.7 -0.24 5.0% 8.5 0 Rural EF 1 6.3 5.9 -0.28 9.5% 8.5 0.25 Rural EF 2 3.5 5.5 -0.27 8.5% 8.5 0.25 Rural EF 3 2.6 5.1 -0.26 7.5% 8.5 0.2 Rural EF 4 1.2 4.7 -0.25 6.0% 8.5 0.15 Notes: Total of ~12.6 million household (HH) utility customers in Maharashtra, India in 2004-05.

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Table B-2: Block Tariff Components: Maharashtra State Electric Distribution Company Ltd (MSEDCL – 2004-05)

Reliability Tariff Demand Unit Charge Charge Block Charge (Rs) (Rs/kWh) (Rs/kWh) Tax 0 30 2.05 0.46 12% 100 30 3.9 0.46 12% 300 30 5.6 0.46 12% 500 30 6.2 0.46 12%

In Mumbai region, a number of private utilities serve customers. They have different rate structures, but differ mostly in the highest rate categories, which are not separated in this analysis due to data limitations. On average, the rates for most consumers modeled are roughly comparable. I therefore use MSEDCL‟s rates for all customers.

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Table B-3: Household Non-Energy Expenditure Shares

Total Income Other Household budget Group Cereals Food Clothing Wares Durables Medical Education Services Entertainment Transportation Water Housing share 1 16% 45% 9% 7% 1% 4% 2% 5% 1% 3% 1% 5% 86% 2 13% 39% 8% 7% 2% 6% 4% 6% 2% 6% 2% 5% 87% 3 10% 34% 8% 6% 4% 7% 6% 8% 3% 7% 1% 5% 90% 4 8% 31% 7% 5% 6% 7% 8% 10% 3% 9% 1% 5% 91% 5 5% 26% 6% 4% 10% 9% 9% 12% 2% 9% 1% 6% 93% 6 5% 26% 6% 4% 10% 9% 9% 12% 2% 9% 1% 6% 93%

Notes: Based on National Sample Survey 61st round (2004-05). Households in income group 6 are assumed to have the same expenditure shares as those in income group 5, due to unavailable data. Totals don‟t add to 100 because they exclude direct energy (electricity and cooking) costs.

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Table B-4: Generation Cost and CO2 Intensity – Maharashtra Base Case 2004-05

CO2 Production Emissions Cost (kg/kWh) (Rs/kWh) Existing Generation 0.89 1.36 New Coal 1.15 2.5 New Gas 0.42 3.5 New Wind 0 4.5 New Solar 0 9 Diesel 1 NA Distribution Cost 2.14

Sources: Maharashtra Electricity Regulatory Commission for existing capacity, Prayas Energy Group for wind and natural gas, solar from the Ministry for New and Renewable Energy (includes subsidies). Diesel fuel consumption is used only to track emissions from industrial self-generation, not costs.

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Appendix C - Long-Term Demand Response Model (Chapter 3)

In the short-term analysis, welfare changes to households have been calculated assuming households have a fixed appliance stock. Here, I describe the model enhancement I use to test the robustness of the prior results to relaxing this assumption. If households expect electricity price increases to endure, they may mitigate their welfare losses by investing in energy efficient equipment. Households‟ investments decisions depend on a number of parameters, including at least buying preferences, discount rates, available cash, transaction costs, and the appliance characteristics to which they have market access. Data were, however, available only on appliance cost and performance for this scope of analysis. The modeling approach is to assume hypothetically that all households have the same buying criteria, but differ in their market access. The extreme cases are then modeled where only either of the highest income group or the lowest two income groups have access to efficient appliances. This permits the most rigorous test of the robustness of prior results, which are most sensitive to the incremental welfare impact of price changes on these household categories. Furthermore, to eliminate the cash flow constraint, costs are assumed to be payable on an amortized basis over their life, much as they would in reality through a utility incentive program.

Modeling Approach Households gain utility from electricity services, which in turn depends on the number of appliances they have, and the extent to which they utilize them. For a given income level, households have a fixed appliance stock, K (representing as the connected load of this stock, in Watts). This stock K provides a certain fixed capacity for services. That is, per unit of time, the lumens (for lighting), cooling capacity (for air conditioning) are determined by the appliance choice. Households then choose LF

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(hours),97 their utilization of the appliance stock. Together, this yields their level of electricity services:

ESt = (K γ) *LFt

Where γ is a dummy unit of value 1 that maps kW to „service units‟.

Investments in energy efficient appliances involve an upfront fixed cost, and monthly savings in energy use for the life of the investment. An investment in an energy efficient appliance produces a reduction in the maximum power rating needed to deliver the same service as the original. Thus, an investment in period 1 of z kW for a price of pz, reduces the connected load of a household to (K-z), for the same service, thereby reducing electricity consumption in all subsequent time periods to (K- z).LF units. Since individual appliances are not modeled, a composite, single-priced energy efficiency investment choice is represented for each income group, where the price is a weighted average of the energy efficient options for their respective appliance stocks, weighted by their expected savings (kWh). Low income groups invest in efficient lights (CFLs) and fans. High income groups additionally invest in efficient refrigerators and air conditioners.

Household utility depends upon their choice of LF, but the cost of this service depends on LF and z. Thus, their utility can be written as:

But note in a partial equilibrium model with constant technology characteristics, income and consumption characteristics (, ), household‟s consumption choices do not change in each period. Further, they would likely use the same discount rate for both streams of payment (electricity bills and appliance payments). Thus, the investment decision can be solved as a one-period optimization using a levelized annual amortization, p‟z, of the investment in z kW reduction over the lifetime of the

97 LF: Load factor. 126

appliance. In practice, this would be equivalent to a utility program where the utility provides the customer with the appliance and recovers its cost over time by charging a fixed annuity in customer bills. Households then evaluate the trade-off in a year of the amortized investment cost versus the annual reduction in electricity consumption costs.

Implicit in this model is the assumption that price changes are expected to remain through the life of the appliances, which is as much as 10 years in some cases. This is reasonable, given that low carbon investments have lifetimes that exceed ten years. If anything, households may expect prices to increase further, if climate change mitigation is indeed the driver for this policy. To avoid speculation, price increases are based on prevailing voluntary targets by the Indian government (described earlier).

2.2.2 Household Maximization Problem:

1 Max 1  (K . LF )   X  z, LF  

s.t. pxX+p’zz + pe(K-z)LF = I

z < K.v LF < 736 (hours in a month)

Where: p’z= (Rs/kW) amortized monthly energy efficient appliance cost, based on capital cost, life, and discount rate z = Reduction in rating (kW) compared to original appliance (for the same service) v= The maximum reduction in load (percentage), calculated as the weighted average for all appliances used by an income group

2.2.3 Data Data were obtained for household appliances in Delhi, India (Table C-1). These costs would likely be comparable to those in Mumbai, India, but less outside the city. Low income households‟ investments would therefore be underestimated, all else equal.

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However, this assumption offsets the excessive rebound effect from asymmetric price elasticity.

As discussed in the paper, household appliance stock is predicted by income. Thus, so are the energy efficient options from which they would choose. The „composite‟ energy efficient investment option for each household was obtained as a weighted average of the monthly amortized cost (pz), weighted by the expected energy saved in a month, based on the appliances‟ typical usage (Table C-2).

Table C-1: Residential Appliance Investment Costs and Savings

R e frig e ra tio n

Lig hting (fluo re s c e nt la m ps ) F a ns (F ro s t F re e ) Split Air Conditioner TV P ro g ra m / te c hno lo g y T-12 lamp T-5 lamp Conventional Efficient Conventional Efficient Conventional Efficient Conventional Efficient P o wer reqmnt 52 25 70 50 165 108 1,350 1,100 100 75 (Watts )

Appliance life 2 2.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 (yrs ) Us age 1,460 1,460 1,600 1,600 3,329 3,329 800 800 1600 1600 (ho urs /year) Retail market 185 340 800 1,000 12,800 16,200 15,000 25,000 11,000 18,000 price (Rs ./Unit) Amo rtized 2 3 9 .2 8 3 .3 4 9 7 .1 3 3 3 .3 2 3 3 3 .3 Co s t (Rs /kW.Mo ) Expected 3.3 2.7 15.8 16.7 3.3 Savings (kWh/Mo ) Expected 1.97 0.63 1.79 5.00 17.50 Savings (Rs /kWh) Inco me Gro up All All 4,5,6 6 6 Us e Source: Lawrence Berkeley National Labs.

Table C-2: Weighted Investment Costs by Income Group

Income Wtd Investment Cost Group (Rs/kW.Month)

1 169 2 169 3 169 4 407 5 407 6 375 Note: TV excluded from the investment portfolio, due to its high cost (See Table above)

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Appendix D - Household Survey Design

Objective The purpose of this household survey was to gather data on actual household energy service conditions in the state of Maharashtra, with a focus on electricity service and kerosene use. In the case of electricity, the conditions of interest were actual costs and service conditions (including supply interruptions) and households‟ coping mechanisms for dealing with chronic interruptions. In the case of kerosene, the objective was to understand kerosene‟s functions, and households‟ experience with purchasing kerosene from the Public Distribution System (PDS) and the black market.

Approach The survey was mostly a closed, structured survey, with a few open-ended questions about households‟ difficulties with electricity service reliability and with obtaining their subsidized kerosene quotas. The quantitative information from the closed questions was the primary survey outputs that were used in the analysis in Chapter 3 (household income, electricity budgets, hours of interruption and electricity backup arrangements). Information from the open questions was not ultimately used except to confirm inferences from some of the closed questions regarding the motivation behind households‟ purchase decisions for kerosene.

Scope The survey was conducted in 450 households in Western Maharashtra over six districts (indicated by the white dots in the Figure below), which were chosen to sample households falling in all of the utility‟s defined rationing priorities. Feeders are assigned a rationing priority based on revenue potential, with higher priority assigned to those feeders with lower losses/higher profits. Most regions contain feeders predominantly of a particular priority assignment (as indicated in the figure below), but with exceptions. As discussed in Chapter 3, the electric utility uses feeders‟ rationing priority to determine the proportion of hours that households are interrupted during supply shortages.

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Source: Maharasthra State Electricity Distribution Company (MSEDCL), April 2009 Note: Letters indicate rationing priority defined by feeders. Not all

Rationing categories were combined into 3 rationing “regions” (AB, CD and EF) to restrict sample size requirements.

Sampling The sampling approach was a mix of strategic/convenience and random sampling. The districts were selected to sample sufficient households of each rationing priority region in an area with diameter of about 200 km in Western Maharashtra, with Mumbai as its western-most point, so as to limit travel time and costs. Within each district, towns and then neighborhoods or villages were strategically picked so as to get a broad sample of income groups (housing type is a good proxy) on independent feeders (load rationing is done by feeder). Within each neighborhood, houses were randomly selected, first by random selection of streets, followed by alternate houses on alternate sides of the street. Because households were often vacant, or their inhabitants occasionally unwilling to do the survey, the house selection process was not strictly followed.

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Variables Data for the following variables were collected and used in the analysis, either directly or qualitatively. Survey contained additional open-ended questions that were not used in this study, and are therefore not included here.

Variable (s) General Household Household Monthly Expenses House type (size, materials) Total monthly expenses Household members (sex, age) Food and grocery expenses Head of household (occupation, education) Utility expenses (water, telecom and electricity) Household appliances and stoves

Electricity Service Use and Costs Kerosene Use and Costs Connection (meter, third party, hook-up) Monthly purchases (unit cost, quantity, source (subsidized, black market)) Consumption (12-month usage history or Functions (Lighting, water heating, other estimate, livelihood use) cooking) Costs (bill dues or flat fee) Primary/Secondary cooking fuel

Electricity Supply Reliability Rationing Priority region (by location) Interruptions (hours, schedule, seasonal variation) Backup device (type, rating, capacity, cost)

The actual survey questions pertaining to these variables are shown next.

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RELEVANT SURVEY QUESTIONS

1. House Characteristics 1.1 Dwelling Type 1. Slum 2. Chawl 3. Flat 4. House 5. Other ____ (specify) 1.2 Dwelling Size 1.2.1 Number of rooms (incl. bathroom and kitchen) 1.2.2 Total Indoor Area _____ (in multiples of 100 sq ft) 1.2.3 Construction Type (check one for each category) Walls 1. Cement 2. Brick 3. Metal 4. Mud/Dung 5. Other _____ (specify) Ceiling 1. Cement 2. Brick 3. Metal (Tin, Aluminum) 4. Mud/Dung 5. Other _____ (specify)

2. Household Characteristics 2.1 Number of Members in household defined as family members sharing a kitchen (Fill in all blanks, 0 if none) 2.2.1 Adult males ______2.2.2 Adult females ______2.2.3 Children (< 18 years) ______2.2.4 Total ______

2.2 Occupation (job) of household head defined as primary wage earner (Circle appropriate): 1 Self employed (non-wage) a Farmer? Yes/No 2. Wage Employee in organised sector (government or company) 3. Wage employee in unorganised sector b 4. Casual wage, non-agricultural (hourly) 5. Casual wage, farmer (hourly) 6. Pensioner a E.g. a small shop owners, rickshaw driver bE.g. small shops, workshops, tea stalls etc.

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2.3 Education of household head defined as primary wage earner: 1. Illiterate 2. Primary 3. Secondary 4. Diploma 5. Degree (LLB) 6. Post-Graduate

3. Household Expenditure 3.1 House Ownership (owned/rented) 3.1.1 Monthly cost (EMI, if owned, otherwise rent): ______

3.2 Monthly expenditure of the household: ______

3.3 Communication Costs (Rs., monthly – ask for bills) 3.3.1 Land line (Rs)______3.3.2 Mobile phone (Rs) ______3.3.3 Number of phones______3.4 Food Purchase 3.4.1. Groceries ______3.4.2 Restaurants _____

3.5. Cooking Fuel Costs (enter amounts for ALL that apply)

Quantity Price Monthly (Rs/Unit) Cost (Rs) 3.5.1 Piped Gas (Cubic Feet) 3.5.2 LPG cylinder (cylinder) 3.5.3 PDS Kerosene (lit) 3.5.4 Non-PDS Kerosene (lit) 3.5.5 Wood (kg) 3.5.6 Dung (kg) 3.5.7 Charcoal/coal (kg)

3.6 Stove ownership (check ALL that apply) 1. Kerosene 2. LPG 3. Wood 4. Biogas 3.7 LPG cylinder ownership (circle one): 0. 1. 2

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5. Electricity Appliances Appliance Number Rating/Size (Avg) Incandescent bulbs Flourescents tubes Compact Flourescent Radio Fans Television (screen type) Refrigerator (litres) Water heater (litres) Air conditioner Washing Machine Microwave Computer (screen type) Iron Aquaguard

6. Electricity Supply Characteristics

6.1 What type of electricity connection do you have (circle appropriate)? 1. Metered /Flat rate/ Hook-up

6.2 To whom do you pay your electricity bill (service provider)? 1. Distribution Company Circle One: MSEB/Reliance/Tata Power 2. Mula Pravda (cooperative) 3. Third Party (non-utility) IF THIRD PARTY: 6.2.1 Frequency (e.g., monthly, bimonthly, etc) ___ 6.2.2 Rate/Tariff: Fixed Charge (Rs /kW) ____ Energy (Rs/ kWh) ____

6.4 Last Bill Details 6.4.1 Month/Yr _____ 6.4.2 Current Charges _____ 6.4.3 Total Amount Due _____

6.5 Enter usage history from bills for last 12 billing periods, where 1 is the most recent

Month 1 2 3 4 5 6 7 8 9 10 11 12 Units (kWh)

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7. Power Cuts 7.1 Do you experience power cuts? (Yes/No)

7.2 On average, how frequent are power cuts, by season? (Number Per Week)

Average Frequency (per week) Summer Monsoon (Mar-May) (Jun-Aug)

7.3 On average, how long are the power cuts?

Average Duration (per cut, in Hours) Summer Monsoon (Mar-May) (Jun-Aug)

7.4 Do you run a home business? If YES, did the power supply reliability affect your location of residence? How?

8. Alternative Sources of Power 8.1 Lighting 8.1.1 How do you light your house during power cuts (check ALL that apply)? 1. Use candles 2. Use a kerosene lamp 3. Battery operated Torches/Flourescents 4. Do Nothing

8.2 Do you own an inverter/UPS)? (YES/NO) IF YES: 8.2.2 Purchase price ______8.2.3 Brand ______8.2.4 Capacity (watts) _____ 8.2.5 Do you have a maintenance contract for this INVERTER? How much does it cost?

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