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An alternative assessment of global climate policies
Accepted Manuscript Title: An Alternative Assessment of Global Climate Policies Authors: Tarek Atalla, Simona Bigerna, Carlo Andrea Bollino, Paolo Polinori PII: DOI: Reference:
S0161-8938(18)30028-0 https://doi.org/10.1016/j.jpolmod.2018.02.003 JPO 6417
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Please cite this article as: Atalla, Tarek., Bigerna, Simona., Bollino, Carlo Andrea., & Polinori, Paolo., An Alternative Assessment of Global Climate Policies.Journal of Policy Modeling https://doi.org/10.1016/j.jpolmod.2018.02.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
An Alternative Assessment of Global Climate Policies Tarek Atalla, University of London Simona Bigerna, Department of Economics, University of Perugia
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Carlo Andrea Bollino*, Department of Economics, University of Perugia Paolo Polinori, Department of Economics, University of Perugia
*CORRESPONDING AUTHOR: University of Perugia, [email protected]
Abstract
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This paper analyzes alternative pricing schemes for funding investment in climate policies. This paper proposes a new policy scenario, explicitly disentangling the issue of burden sharing of financing new investments from the issue of taxation energy consumption and therefor emissions. We compare traditional allocation schemes with an optimal Ramsey pricing by applying demand elasticity values, derived from empirical estimations of household behavior for the 106 leading countries in the world, representing around 90% of total world energy consumption and carbon emissions in 2014. We calculate country-specific alternative taxation options: uniform, equitable and Ramsey pricing schemes, applied to households, assessing the related welfare effects. Our results show that the optimal pricing scheme, for a given investment need, can improve world welfare at the expenses of equitable considerations. In addition, the aggregate societal benefit outweighs the losses associated with specific group of countries, paving the way for easier political agreement, using compensation schemes to redistribute the proceeds.
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Keywords: demand elasticity; carbon price; heterogeneous consumers’ behavior; optimal Ramsey prices, COP 21
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JEL Category Selection: D11 D12 C10 H21 Q41
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1. Introduction Climate policies ultimate goal is to mitigate global warming and limit the role of climate change as a new type of global externality (Weitzman, 2015, 2016), which is characterized by (i) the
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multifaceted nature due to several pollutants which concur to global warming; ii) the high number of stakeholders involved; iii) the unprecedented degree of coordination and cooperation required to solve such issue.
In the past scholars have mainly focused on carbon pricing as a crucial instrument to achieve emission reductions (Fischer and Morgenstern, 2006; Alberola et al., 2009; Weitzman, 2015),
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designing policy architectures for abatement purposes (Bréchet and Lussis, 2006; Seidma, and
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Lewis, 2009; Rogelj et al. 2013; Kennedy and Basu, 2014; Han et al., 2015) and also for
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promotion of new zero or low-carbon technologies (Benz and Trück, 2009; Goel and Hsieh,
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2006; Newbery, 2016).
Recently, Nordhaus (2015) and Weitzman (2016) have focused the analysis on the hardships
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associated with obtaining a global international agreement because of free-riding behaviors,
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advocating the necessity of a new policy architecture to set up a “World Climate Assembly”.
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Since the Kyoto Protocol, the limited success international political meetings (Weitzman, 2015; 2016) has been plagued by bitter confrontation between developed and emerging economies on a
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fair economic allocation of the burden associated with carbon emissions reduction, taking into consideration the heterogeneity of the populations involved (Hassett and Metcalf, 1995; Metcalf
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and Weisbach, 2009). Coordination attempts among countries to cope with climate change failed to reach a politically viable implementation of the classical solution of computing a Pigouvian taxation based on the criterion of adding marginal social damage to the marginal private cost. The aim of this paper is to design an economically optimal carbon price to finance investments in
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climate policies, which can maximize the likelihood of reaching a political consensus. In this sense, we advocate for a more general energy tax concept that can financially settle the cost for mitigating damages associated with energy usage, rather than a carbon price per se. This paper
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proposes an optimal tax scheme to finance a portfolio of different mitigation policies, which mainly requires new technologies investments and innovation, not only a mere energy demand reduction.
This is the main motivation to support our optimistic claim that such scheme can be politically acceptable: we shift the policy focus from the unique target of reducing or limiting the energy
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consumption to a complementary target of allocating in the most efficient way the burden to
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support new investment in new technologies and actions that will ultimately bring about climate
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change mitigation. In general, a mark-up on top of energy prices to raise such financing involves
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the dilemma of equity vs. efficiency, but we focus only on efficiency through a Ramsey (1927) pricing scheme, which minimizes the deadweight losses (DWL) associated with given market
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inefficiencies. In other words, a Ramsey pricing mechanism has the advantage of maximizing
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efficiency for a given fund raising target.
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Surprisingly, the vast literature on climate policy has not explored this analytical tool as a mechanism for sharing the cost burden of climate policy and for setting an efficient way to
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achieve an international consensus among countries for the promotion of an effective climate change policy (Buchner and Carraro, 2005).
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We contribute to the literature in two ways. First, we make explicit use of households’ preferences, as expressed through their energy demand behavior. This is a more complex, yet more accurate way to quantify the ‘polluters pay’ principle. Households are the final consumers of goods and services and consumption goods incorporate energy used in the production process,
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whether they are produced domestically or imported. In addition, households are the ultimate owners of the corporate sector and the final beneficiary of government expenditures. Accordingly, allocation of a tax burden based on households’ consumption is a more precise
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scheme to account for all of the energy incorporated in the society’s economic activity. Second, we explicitly disentangle the issue of burden sharing of financing new investments from the issue of taxation energy consumption and therefor emissions. We propose a more understandable and transparent tool that allows to implement a more effective global policy that is focusing on new technology investment incentives and related sharing mechanism, and not
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directly on the issue of emission reduction. Indeed, the recent dissatisfactory records of the
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international policy forums call for exploring alternative solutions to be compared with the
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mainstream ones.
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The paper is organized as follows. Section 2 presents a brief review of the related literature. Section 3 presents the policy strategies and briefly describes the methods and data set used.
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Section 4 presents the empirical results and the discussion of the alternative schemes. Section 5
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presents conclusions and policy implications.
2. A discussion of the literature related to Ramsey pricing applications
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Policy makers have long studied opportunities for emissions reductions, spurring a plentiful of literature offering solutions to implement carbon price (see among others: Fischer and
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Morgenstern, 2006; Benz and Trück, 2009; Cooper, 2010; Cramton et al. 2015; Metcalf and Weisbach, 2009; Nordhaus, 2007; 2013; Han et al., 2015 Calel and Dechezleprêtre, 2016)). Operationally, there have been the EU carbon emissions trading system (EU ETS) in 2005, substantially choked by world recession of 2009 (Hu et al, 2015), declaration by China to reduce
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its power sector emissions by 60% by 2020, stop emissions growth by 2030, and cut carbon intensity by 45% based on 2005 levels (UNFCCC, 2016). However, most of the climate policies already implemented are local or regional; global agreements have faced stiff opposition. The
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agreement at COP 21 marks a turning point, but there is still disagreement about the allocation of the financial burden.
Applying the Ramsey pricing mechanism in the energy sector may offer a solution for an efficient allocation, because Ramsey pricing minimizes the welfare loss associated with taxation. This mechanism has been applied in tariff regulations for public utility sectors, such as
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telecommunications, transport, pharmaceuticals, electricity and gas distribution (Laffont and
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Tirole, 1996), oil refining sector Babusiaux and Pierru (2007), optimal California gasoline tax
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proposal by Lin and Prince (2009) and airport operation cost recovery (Hakimov and Mueller,
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2014). Many application of Ramsey pricing have analyzed the heterogeneity of demand elasticity behavior to justify charging differentiated prices to different groups of customers, such as the
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analysis of residential electricity customers in the US (Berry, 2002), China (Qi et al., 2009, Sun
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and Li, 2013), Russia (Nahata et al. 2007), Brazil (Santos et al., 2012), Japan (Matsukawa et al,
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1993) and European countries (Deeney et al., 2016). Most of these studies conclude that the actual tariffs are at variance with the optimal Ramsey
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pricing scheme. More recently, other studies focused on Ramsey pricing to optimally allocate the social cost of externalities, such as the environmental cost of air traffic (Martín-Cejas, 2010), or
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electric network congestion and security management (Bigerna and Bollino, 2016). Van der Ploeg (2016) and Boeters (2014) discuss various options for the optimal carbon taxation.
Three main observations follow from the literature review. First, policy actions tend to impose inefficient price schemes. Typical examples are the different tariff structures for residential and
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industrial electricity users, the various tax rates on gasoline and diesel in the transport sector and the multiple tax rates on electricity and natural gas for residential consumers. These examples raise the question of why policy actions are inefficient and why policy-makers do not use
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Ramsey schemes. Second, Ramsey pricing maximizes efficiency, but does not take into account equity across groups. Typically, poor consumers are less price elastic because they cannot afford flexible behavior, often in the form of more efficient capital stock. This raises questions about the costs of non-efficient solutions and the terms of the equity-efficiency dilemma1.
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Third, implementation of the Ramsey pricing worldwide given that a carbon price regulation is
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needed may prove difficult, even if it is welfare enhancing and good for producers. Regulatory
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actions might face different difficulties due to consumers and technologies heterogeneity.
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However, in practical terms, implementation of Ramsey pricing scheme within countries is more difficult than between countries, as highlighted in studies on investment required for worldwide
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public goods (see among others Danzon and Towse, 2003).
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From a political viewpoint, it would be possible to explain to the population that differential
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pricing may be advantageous and not detrimental to their interest, in case of a positive spur to global incentives to R&D in decarbonization technologies. Furthermore, in poor and emerging
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countries, it could be useful to explain that as income grows and the price elasticity changes, the
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regulatory scheme will be adequate to the actual economic scenario.
3. Policy strategies 3.1 The traditional policy scenarios 1
Additionally, the status-quo tariff structure is the result of historical lobbying by different constituencies. The strongest constituencies may oppose tariff changes and it may be politically difficult to find political legitimacy for changing the tariff structure based on innovative empirical findings.
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In order to directly penalize carbon emissions, the policy-driven carbon price has to be different from the private marginal costs, pointing to the classical solution for solving market distortions based on the equi-marginal principle. This solution remains short of providing efficiency
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financing for other climate policies. The aim of fostering renewable energy, its least cost deployment, energy efficiency measures, research and other innovative technologies (e.g., carbon capture and storage) needs to be financed. This is also complemented by the uncertainty about the adequate measuring of the price of the social cost of carbon (Tol, 2011).
The approach used so far involves designing the individual country commitments in proportion
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to emissions or GDP, valued at a common marginal price. This is undoubtedly the optimal
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solution, for carbon pricing mainly aims to (fully) internalize the social costs of carbon rather
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than generate revenue for funding emission mitigation technology. However, in some economic
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circles, this could be considered as a proxy trade barrier. The main shortcoming of this approach is that it creates a burden for newly industrialized countries that produce goods ultimately
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consumed by advanced economies. In other words, energy intensive manufacturing countries risk
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(Gasim (2015).
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to be penalized for the carbon content of the final goods consumed by higher income economies
Consequently, the question is in finding the optimal way of achieving a worldwide win-win
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solution, given that strong international governance is needed, because the specific country taxation needs to be imposed or agreed upon in a supra-national context such as COP 21
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(Buchner and Carraro, 2005; Bollino and Micheli, 2014).
3.2 The new policy proposal We propose a new policy strategy that can be used to allocate a financial burden among world countries to support any investment requirement at the world scale. We assume that
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heterogeneous consumers value the marginal damage of greenhouse gas emissions differently, due to differences in interest, perception income and values across the world 2 and we clearly separate the issue of designing a complex carbon pricing policy that reduces energy consumption
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(and therefore emission) from designing an operational method for allocating financing among countries.
The new policy computes each country’s share of the cost of climate change mitigation, independent of how that overall cost is calculated. This policy proposal can be applied to finance two main investment policies, which are now under discussion in the international agenda: 1) the
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consensus cost estimate of the level of investment necessary to achieve the 450 ppm Scenario,
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according to the New Policies Scenario developed by the International Energy Agency (IEA,
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2016) by 2035; 2) the COP21 agreement to provide financial aid to poorer economies by 2020.
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The consensus quantification of the investment cost of the New Policies Scenario is around 1.5% of world GDP. The COP21 agreement reached in Paris on December 2015 (United Nations,
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2015) been confirmed recently by the conclusion of the COP23 in Bonn in November 2017. The
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agreement quantifies an explicit target for financial aid and will enter into effect once ratified by
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55 countries that represent at least 55% of GHG emissions. The agreement stipulates financial aid of at least USD $100 Billion to poorer economies by 2020, a sum set to potentially increase
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in the future. However, the agreement does not describe a path forward on the allocation of donor country contributions, nor which countries will receive financial aid. Many possible
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combinations of the 195 UNFCCC3 participating member countries could bring the agreement
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For example, since carbon emissions are of greater concern to children (given that they are likely to live long enough to see greater impact from climate change), an older society, such as Europe, is likely to have a different perception than a younger society, like some emerging countries where half of the population is less than 16 years old. 3 The UNFCCC stands for the United Nations Framework Convention on Climate Change
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into effect. We consider a plausible agreement that includes the top emitting countries and the richest countries in terms of GDP per capita, representing at least 55% of total emissions.
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3.2. Methods and Data We analyze different pricing schemes, which are: a uniform price taxation, levied in proportion to the country’s importance in terms of world GDP and/or carbon emissions; an efficient Ramsey pricing scheme; an equitable pricing scheme, levied only on the richest subset of countries.
We construct data for the household sector by considering the final consumption expenditure
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composed of two goods, energy consumption and other goods consumption, for 106 countries for
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the period 2000 to 2013. Quantities of energy consumption (in Ton of Oil Equivalent, Toe)
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include both energy for residential uses and energy for transportation, representing the actual
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direct expenditure of households for all energy uses (in constant 2005 US dollars). This allows us to capture the households’ preferences for direct energy use for all households needs.
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We model aggregate consumption demand of the household sector, assuming cost minimization
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behavior of the individual heterogeneous agent. Our hypothesis considers each country as a
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representative agent that rationally optimizes simultaneous choice of a bundle of goods, which is based on the aggregation of heterogeneous agents (Deaton and Muellbauer, 1980) within each
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country. Empirical estimation (available in detail in Atalla et al. 2017) allow to evaluate t household price elasticity of energy consumption, which is directly related to emissions. The
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elasticity is a revealed preference measure of the willingness to pay for energy consumption, and indirectly, for the willingness to pay for emissions reductions. The inverse of the demand elasticity is used to compute the Ramsey proportionality factor to compute the burden sharing of
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the world climate policy for each country. The DWL associated with the Ramsey solution are compared to other traditional burden sharing mechanisms.
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4. Empirical results and discussion on alternative carbon pricing options It is increasingly accepted that COP 21 agreement will be not be alone sufficient in getting climate change mitigation. Indeed, if the aim is to keep global warming below 2° C over the long run, COP 21 allows to achieve only half of the emission reduction needed. However, this goal can be reached with additional mitigation policies (Vandyck et al., 2016).
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We design alternative carbon price taxation options, taking as a constraint the amount needed
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worldwide for climate policy. The latest IEA Scenarios (IEA, 2016) envision USD $100 billion
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of investment per year as necessary to support mitigation policy to stabilize atmospheric CO2
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concentration at 450 ppm by 2030. In every scenario, we compute the allocation of this investment among countries according to alternative taxation options. The resource constraint is
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as follows: we consider that the amount of USD $100 billion (in real 2005 dollars) is on average
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0.2% of world GDP, 0.3% of total household expenditure worldwide, or 2.5% of total household
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energy expenditure worldwide.
We take the viewpoint of the international group of countries who undersign COP21 Agreement
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interested in a political consensus to promote climate policy based on two pillars: global efficiency and clear distributional commitment among countries.
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4.1. Preliminary results We construct seven alternative scenarios of energy price taxation on the household sector in each country (Table 1). In each scenario, we introduce a surcharge on the existing energy taxation. [Table 1]
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The taxation revenue worldwide is constrained by the policy target in all Scenarios. The first five Scenarios are designed to encompass global policy financing.
The last two
Scenarios consider only the donor policy to the poorer countries. We report the detailed results
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for all Scenarios in Tables 2 and 3. [Table 2] [Table 3]
In detail, Scenario 1 designs a taxation burden for each country that is proportional to each country’s share of world GDP. In Scenario 2, the allocation burden is proportional to each
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country’s total household consumption expenditure and in Scenario 3 the allocation burden is
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proportional to each country’s household expenditure on energy. This Scenario imposes the
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equimarginal principle. In scenario 4, the allocation burden is proportional to each country
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carbon emissions. Each of these four Scenarios imposes a taxation burden proportional to a measure of the size of each country as part of the world total. Scenario 5 is the optimal Ramsey
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pricing scheme based on the estimated price elasticities. We compute optimal Ramsey prices for
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every country using the inverse of the absolute value of the energy demand elasticity (Column 5
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of Table 2).
Scenarios 6 and 7 consider only the top countries in term of emissions and of GDP per capita, in
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the spirit of the recent Paris COP21 agreement. We identify a group of at least 55 countries that represent at least 55% of the world emissions, selected according to the highest GDP per capita.
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In this way, we identify the top 67 richest countries, as listed in table 3. In particular, in Scenario 6 we compute the burden share of participating countries using the GDP shares and in Scenario 7 we use the optimal Ramsey shares.
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For all Scenarios, we compute the DWL in each country associated with different taxation schemes. The DWL is the area under the energy demand function for each Scenario between the pre- and post-taxation price of energy, after the imposition of the taxation surcharge.
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Scenarios 1 through 4 are variants of the proportional principle, where each country shares the world burden in proportion to the size of its GDP, household consumption expenditure, household energy expenditures and carbon emissions, respectively. This implies that the weight of each country in total world wellbeing forms the basis for its contribution to climate policy cost.
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On the other hand, Scenario 6 is founded on household behavior, as reflected in energy price
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elasticity, to minimize welfare losses resulting from the implemented policy action. In this case,
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each country’s contribution is based on its human and economic behavioral decision making,
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irrespective of macro-economic variables such as GDP or carbon emissions. The optimal taxation regime based on this scenario may lead to inequalities, as poorer developing countries
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that use outdated and inefficient physical capital, and thus have lower demand elasticity, may
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end up being taxed a higher portion of their income. Equation 5 reflects this. An interesting result
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from Scenario 5 is that optimal taxation would impose a lower burden on leading polluters such as US, Japan, Brazil, UK, France and Italy and a higher burden on China, India and Russia.
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We report the results of Scenarios 6 and 7, in which the 67 richest countries, as measured by GDP per capita, share the burden of climate policy in Table 3. This group includes China, which
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has the lowest GDP per capita in the group but is the largest emitter. In total, this subset of 67 countries represents around 80% of 2014 global carbon emissions. Both scenarios require a large threshold of countries to account for the constraint of the emissions requirements, especially when compared to Scenario 1.
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The burdens for each country vary among the scenarios. In Scenario 1, the US and China have a share of 29.5% and 7.8%, respectively. These values are 22.8% and 9.6% in Scenario 3, respectively. These results show that China has to pay more in the equimarginal case than in the
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GDP share case. The values for these countries decrease to 22% and 6% in Scenario 5; 28% and 8% in Scenario 6 and 27% and 10% in Scenario 7, respectively. These results show that China’s tax burden varies considerably, and more so when using the Ramsey scheme in Scenario 7 compared to Scenario 5. This shows that China must pay a high price to participate to the club of the richest countries, and thus the group of top donors, in the efficient Ramsey taxes allocation.
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4.2. Welfare analysis
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We report the DWL associated with scenarios 1 and 5 in Table 2, while DWL for scenarios 6 and
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7 are reported in table 3. Not surprisingly, the loss associated with Ramsey pricing is the least, as
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the methodology’s goal is to reduce economic inefficiencies. A proportional carbon tax in all countries yields a DWL around five times larger, while an equitable scheme that charges higher
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tax shares according to GDP yields an even greater loss.
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These elements are assessed in further details below. Table 4 shows the DWL associated to
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different scenarios for the main five macro-regions (Panel a). According to the proportional principle, the US contribute with higher welfare loss regardless of the number of countries
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considered. On average (Panel b) the losses of other macro-regions lie between 0,04% and 21%
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of US DWL.
[Table 4]
Using the Ramsey principle EU28 becomes the macro-region with the higher DWL while the US moves to the second position. Ramsey method not only consistently reduces the inefficiency
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associated to the proportional principle but it changes the relative positions in term of macroregions’ burdens also reducing the disparities among macro-regions. Inefficiency reduction due to Ramsey pricing is an important driver to free resources.
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Considering the amount of DWL saved for each macro-region (Table 5), it is interesting to highlight that the amount saved is noticeable greater than the aggregate DWL lost by the losers both considering all the countries or the subgroup of top countries in term of emissions and of GDP per capita. [Table 5]
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Considering all the individual countries, only one of the top 10 winner countries could be
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sufficient (Figure 1) to compensate the aggregate DWL of the all 66 losers. Indeed, the aggregate
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welfare measure is equal to 35.75 that is lower than the India DWL avoided (49.42).
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[Figure 1]
Consequently, the contribution supported by the losers would be largely compensated by the
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efficiency gains obtained by the richer and heavy-polluting countries. In other words, the
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Ramsey pricing mechanism can maximize the likelihood of reaching a political consensus on
abatement.
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how to foster and finance the technological change in order to reduce the long-run cost of
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Results do not change taking into account scenarios 6 and 7, which consider only the donor policy to the poorer countries. In this case the additional aggregate DWL should also include the
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DWL associated to the equi-proportional principle for the poorest countries (scenario 1) and the DWL due to the Ramsey pricing for the new losers that arise in scenarios 6 and 7. This means that the DWL that should be compensated amounts to 47.05, (35.75 + 10.55 + 0.75),
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respectively. Also in this case the DWL saved by the richest countries is more than sufficient to compensate the welfare reduction that affects poorest countries (Figure 2). [Figure 2]
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Indeed, the last of the top ten countries, South Korea, avoids more than 50% of compensation required by all the other countries: poorest and new losers. This confirms that it could be possible to achieve a worldwide win-win solution with differential carbon pricing to support low zerocarbon technologies pursuing an efficiency policy.
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5. Conclusion and Policy Implications
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This paper has proposed a new policy to finance climate change mitigation actions, such as
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fostering renewable energy, its least cost deployment, energy efficiency measures, research and
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other innovative technologies. Alternative pricing schemes aimed at funding investment in climate policies have been compared: traditional allocation schemes with an optimal Ramsey
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pricing by applying a complete demand system for the world households’ consumption behavior
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and used the resulting country price elasticity values to compute an optimal Ramsey price
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scheme to finance climate policy.
Empirical results lead to two implications. First, the new policy can provide to policy makers a
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quantitative assessment of the relative efficiency of different policy actions. Our proposal shows an efficient worldwide taxation scheme to fund investments to spur climate policy actions. The
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taxation strategy depends crucially on the heterogeneity of household behavior in countries around the world. Therefore, policy actions could be designed around the efficiency principle, with eventual compensations for political reasons, rather than on a debatable equity principle that leads to greater economic inefficiency.
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Second, our empirical estimation shows significant differences in the burden allocation when the allocation impinges only on the world’s richest countries. Our optimal pricing scheme applied to the group of richest countries shows that some countries could pay a high “access price” to
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adhere to this group of richest countries. Therefore, we warn about the need, through political negotiations, to assess the risk of opportunistic behavior by countries attempting to avoid this high price. Furthermore, the combined global societal benefit outpaces the resulting losses associated with some countries which opens the door for potential compensation through a redistribution of proceeds.
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The issue of raising taxation is a difficult task for the policy maker, because it involves inevitably
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distortions. In general, the policy maker is confronted with funding limitations and the associated
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efficiency-equity trade-off. Equitable pricing policies have a role in improving the living
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conditions of poor households, but usually impose a societal cost in terms of market inefficiency. On the contrary, when implementing policies to promote maximum economic efficiency, the
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poorest in the society often get hurt.
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In this respect, the new policy proposal can show to the policy maker the quantitative range of
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the associated efficiency-equity trade-off, spanning from one extreme of maximum efficiency to the other extreme of maximum equity. The estimations of demand elasticities are useful to
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construct the minimum distortion pricing policy (the so-called Ramsey pricing), the other extreme being the maximum equitable solution, based on countries relative importance, in terms
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of the share of total world GDP and/or household expenditure and/or carbon emissions (the equimarginal principle).
On another level, the same policy maker needs to assess the economic impact associated with equitable intervention, like price subsidy to the poor and elderly. We provide such measure in
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terms of DWL. This result suggests that governments should base their policies more on the behavior of their populations rather than abstract technical standards defined by bureaucrats. In conclusion, this paper advocates a new policy stance worldwide, using the estimated consumer
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demand elasticity as a novel basis to differentiate taxation worldwide to finance global policies aimed at climate change mitigation. Policy makers should be aware that they will face politically responsible economic agents who need efficient and welfare-improving proposals to pay for
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investments to mitigate climate change.
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Goel, R.K., Hsieh E.W.T. (2006) On Coordinating Environmental Policy and Technology Policy. Journal of Policy Modeling 28: 897-908
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Hakimov, R., Mueller, J. (2014) Charges of Uncongested German Airports: Do They Follow Ramsey Pricing Scheme? Research in Transportation Economics 45: 57-65.
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Han, S.K., Ahn, J.J., Oh, K.J., Kim, T.Y. (2015) A New Methodology for Carbon Price Forecasting in EU ETS. Expert Systems 32: 228-243.
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Hassett, K.A., Metcalf. G.E. (1995) Energy Tax Credits and Residential Conservation Investment: Evidence from Panel Data. Journal of Public Economics 57: 201-217.
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Hu, J., Crijns-Graus, W., Lam, L., Gilbert, A. (2015) Ex-ante Evaluation of EU ETS During 2013–2030: EU-internal Abatement. Energy Policy 77: 152-163. International Energy Agency (IEA) (2016) World Energy Outlook 2015, Paris: IAE. Laffont, J.J., Tirole, J. (1996) Creating Competition Through Interconnection: Theory and Practice. Journal of Regulatory Economics 10: 227-256.
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Lin, C.Y.C., Prince, L. (2009) The Optimal Gas Tax for California. Energy Policy 37: 51735183. Kennedy, M., Basu, B. (2014) An Analysis of the Climate Change Architecture. Renewable and Sustainable Energy Reviews 34: 185-193.
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Martín-Cejas, R.R. (2010) Ramsey Pricing Including CO2 Emission Cost: An Application to Spanish Airports. Journal of Air Transport Management 16: 45–47. Matsukawa, I., Madono, S., Nakashima, T. (1993) An Empirical Analysis of Ramsey Pricing in Japanese Electric Utilities. Journal of the Japanese and International Economies 7: 256-276. Metcalf, G.E., Weisbach D. (2009) Design of a Carbon Tax. Harvard Environmental Law Review 33: 499.
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Nahata, B. Izyumov, A. Busygin, V. Mishura, A. (2007) Application of Ramsey Model in Transition Economy: A Russian Case Study. Energy Economics 29: 105-125.
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Newbery, D.M. (2016) Towards a Green Energy Economy? The EU Energy Union’s Transition to a Low-carbon Zero Subsidy Electricity System –Lessons from the UK’s Electricity Market Reform. Applied Energy 179: 1321-1330.
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Nordhaus, W. (2015) Climate Clubs: Overcoming Free-riding in International Climate Policy. American Economic Review 105: 1339-1370.
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Qi, F., Zhang, L.Z., Wei, B., Que, G.H. (2009) An Application of Ramsey Pricing in Solving the Cross-subsidies in Chinese Electricity Tariffs. IEEE, Institute of Electrical and Electronics Engineers (May 2008): 442–447. Ramsey, F.P. (1927) A Contribution to the Theory of Taxation. Economic Journal 37: 47-61.
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Rogelj, J., McCollum, D. L., Reisinger, A., Meinshausen, M., Riahi, K. (2013) Probabilistic Cost Estimates for Climate Change KMitigation. Nature 493 (7430): 79.
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Santos, P.E.S., Lima, J.W.M., Leme, R.C., Ferreira, T.G.L. (2012) Distribution Charges for Consumers and Microgeneration Considering Load Elasticity Sensitivity. Energy Economics 34: 468-475.
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Seidman, L., Lewis, K. (2009) Compensations and Contributions Under an International Carbon Treaty. Journal of Policy Modeling 31: 341-350. Sun, C., Boqiang, L. (2013) Reforming Residential Electricity Tariff in China: Block Tariffs Pricing Approach. Energy Policy 60: 741-752. Tol, R.S.J. (2011) The Social Cost of Carbon. Annual Review of Resources Economics 3: 419443. 20
United Nation (2015) Conference of the Parties COP21. FCCC/CP/2015/L.9/rev1. Accessed on April, 16th 2016 via https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf
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UNFCCC (2016) Communication from the Chinese Delegation on Enhanced Actions on Climate Change: China’s Intended Nationally Determined Contributions. Accessed on April 16th, 2016 via www4.unfccc.int/submissions/INDC/Published%20Documents/China/1/China's%20INDC%20% 20on%2030%20June%202015.pdf Van der Ploeg, F. (2016). Second-best Carbon Taxation in the Global Economy: The Green Paradox and Carbon Leakage Revisited. Journal of Environmental Economics and Management 78: 85–105. Weitzman, M.L. (2015) Internalizing the Climate Externality: Can a Uniform Price Commitment Help?. Economics of Energy & Environmental Policy 4: 37-50.
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Weitzman, M.L. (2016) Voting on Prices vs. Voting on Quantities in a World Climate Assembly, Research in Economics: https://doi.org/10.1016/j.rie.2016.10.004.
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4500 4000
3894.57
3500 3000 2500 1500 787.17
1000
526.75
500
259.19 132.54 130.23 120.36 63.7
53.39 49.42
U
0
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2000
M
A
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Figure 1: Dead weight loss avoided comparing scenario 1 vs. 5; top ten countries
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6000 5334.67
TE
5000 4000
2000 1000
1079.28
731.91
363.89 185.78 183.85 167.67 88.35 75.63 67.34
A
CC
0
EP
3000
Figure 2: Dead weight loss avoided comparing scenario 6 vs. 7; top ten countries
22
Table 1: Description of various scenario implemented Scenario
Description Allocation based on GDP shares
2
Allocation based on Household Consumption Expenditure shares
3
Allocation based on Household Energy Expenditure shares
4
Allocation based on Carbon emissions shares
5
Allocation based on Ramsey optimal pricing
6
Allocation based on the GDP shares of top 68 countries as per COP21
7
Allocation based on Ramsey optimal pricing of top 68 countries as per COP21
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1
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Table 2: Alternative taxation options –allocation shares by countries worldwide -GDP and population shares (average 2008-2012). Numbers between parentheses correspond to scenarios (1) GDP
(2) Total Expend
(3) Energy Expend
(4) Carbon Emission
(5) Optimal Ramsey
Deadweight loss in Scenario (1)
Deadweight loss in Scenario (5)
Population share
Albania Algeria Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Bolivia Bosnia-Herz. Brazil Bulgaria Burkina Faso Cambodia Cameroon Canada Chile China Colombia Congo DR Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Rep. Denmark Dom. Rep. Ecuador Egypt El Salvador Ethiopia Finland France Gabon Gambia Georgia Germany Ghana Greece
0.02 0.24 0.01 1.66 0.68 0.05 0.04 0.16 0.09 0.82 0.02 0.03 2.21 0.07 0.01 0.02 0.04 2.48 0.31 7.87 0.38 0.02 0.05 0.04 0.10 0.11 0.04 0.31 0.53 0.09 0.09 0.25 0.00 0.04 0.42 4.54 0.02 0.00 0.02 6.11 0.04 0.49
0.03 0.13 0.02 1.67 0.56 0.05 0.03 0.23 0.20 0.66 0.03 0.04 2.80 0.10 0.01 0.02 0.05 2.33 0.36 5.18 0.45 0.08 0.07 0.05 0.10 0.09 0.04 0.22 0.42 0.14 0.14 0.42 0.06 0.11 0.36 4.06 0.01 0.00 0.03 5.13 0.11 0.54
0.04 0.08 0.01 1.30 0.70 0.04 0.02 0.08 0.16 0.83 0.04 0.04 2.07 0.14 0.10 0.08 0.20 2.38 0.45 9.57 0.30 0.26 0.06 0.18 0.14 0.02 0.06 0.42 0.66 0.14 0.08 0.12 0.07 0.30 0.50 3.76 0.06 0.01 0.03 6.52 0.38 0.54
0.02 0.40 0.02 1.21 0.21 0.11 0.08 0.19 0.21 0.32 0.05 0.08 1.44 0.16 0.01 0.01 0.02 1.59 0.26 29.57 0.24 0.01 0.03 0.02 0.07 0.12 0.02 0.36 0.13 0.07 0.12 0.72 0.02 0.02 0.18 1.11 0.01 0.04 0.03 2.39 0.03 0.28
0.02 0.05 0.00 1.03 0.46 0.01 0.04 0.09 0.35 0.61 0.03 0.08 0.94 0.35 0.05 0.06 0.36 3.26 0.60 5.55 0.37 0.40 0.06 0.83 0.56 0.01 0.03 2.03 0.44 0.10 0.04 0.09 0.04 0.74 0.38 2.98 0.03 0.00 0.01 6.14 0.38 0.41
-0.01 -0.20 0.00 -17.39 -4.55 -0.04 0.00 -0.07 -0.04 -5.99 -0.01 0.00 -65.22 -0.02 -0.02 -0.01 -0.03 -27.36 -0.66 -540.46 -0.58 -0.02 -0.02 -0.01 -0.02 -0.01 -0.03 -0.17 -3.30 -0.12 -0.09 -0.25 0.00 -0.03 -1.76 -137.36 -0.01 0.00 -0.01 -269.11 -0.09 -2.25
-0.03 -0.09 -0.01 -1.66 -0.75 -0.02 -0.06 -0.14 -0.57 -0.99 -0.05 -0.13 -1.52 -0.56 -0.08 -0.10 -0.59 -5.28 -0.97 -13.71 -0.60 -0.65 -0.10 -1.34 -0.90 -0.02 -0.05 -3.28 -0.71 -0.16 -0.07 -0.14 -0.07 -1.20 -0.61 -4.82 -0.06 0.00 -0.02 -9.92 -0.61 -0.66
0.05 0.58 0.05 0.36 0.14 0.14 0.02 2.43 0.15 0.18 0.16 0.06 3.18 0.12 0.26 0.23 0.32 0.56 0.28 21.81 0.75 1.06 0.07 0.32 0.07 0.18 0.01 0.17 0.09 0.16 0.24 1.32 0.10 1.35 0.09 1.06 0.02 0.03 0.07 1.33 0.41 0.18
U
N
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EP
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A
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Country
24
A
N
-0.40 -0.18 -0.05 -0.57 -4.49 -1.67 -0.31 -3.50 -8.21 -0.07 -0.08 -4.04 -0.02 -0.12 -0.06 -0.06 -0.24 -0.17 -1.17 -0.01 -0.03 -1.34 -0.02 -0.02 -0.13 -0.43 -2.55 -0.43 -0.14 -8.72 -0.43 -0.04 -2.33 -0.07 -0.09 -0.41 -1.25 -4.05 -0.51 -0.05 -1.12 -3.96 -0.04 -0.36 -0.36 -0.19 -0.27 -0.76 -1.73 -2.62
0.23 0.16 0.12 0.16 19.41 3.91 0.07 0.99 2.08 0.10 0.26 0.67 0.09 0.04 0.07 0.10 0.05 0.01 0.46 0.01 0.06 1.85 0.06 0.04 0.52 0.38 0.27 0.07 0.25 2.58 0.08 0.05 2.83 0.06 0.11 0.47 1.52 0.63 0.17 0.03 0.35 2.32 0.17 0.44 0.12 0.09 0.03 0.81 0.81 0.75
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-0.17 0.00 -0.02 -0.51 -53.91 -5.73 -1.81 -123.86 -795.38 -0.03 -0.24 -0.04 0.00 -0.02 -0.03 -0.02 -0.02 -0.05 -0.46 0.00 0.00 -31.42 0.00 0.00 -0.43 -0.01 -7.63 -0.36 0.00 -1.06 -3.45 -0.03 -0.22 -0.03 0.00 -0.33 -0.63 -3.57 -1.93 -0.02 -0.23 -17.18 0.00 -0.37 -0.02 -0.15 -0.05 -4.65 -49.79 -56.01
U
0.25 0.11 0.03 0.35 2.78 1.03 0.19 2.16 5.08 0.04 0.05 2.50 0.01 0.07 0.04 0.03 0.15 0.11 0.73 0.01 0.02 0.83 0.01 0.01 0.08 0.27 1.58 0.26 0.09 5.40 0.27 0.02 1.44 0.04 0.06 0.25 0.77 2.50 0.32 0.03 0.69 2.45 0.03 0.22 0.22 0.12 0.16 0.47 1.07 1.62
A
0.04 0.01 0.03 0.16 6.80 1.85 0.12 1.30 3.89 0.07 0.86 0.04 0.02 0.03 0.07 0.13 0.05 0.04 0.74 0.01 0.01 1.53 0.02 0.06 0.19 0.01 0.55 0.10 0.00 0.29 0.15 0.21 0.54 0.03 0.02 0.17 0.27 1.04 0.16 0.28 0.28 5.93 0.00 1.71 0.16 0.11 0.05 1.56 1.93 0.89
M
D
0.32 0.06 0.07 0.35 3.05 1.08 0.36 3.41 8.20 0.07 0.11 0.59 0.02 0.07 0.06 0.03 0.10 0.10 0.38 0.01 0.02 1.46 0.02 0.02 0.19 0.12 1.16 0.25 0.05 1.67 0.47 0.04 0.43 0.06 0.04 0.24 0.53 1.34 0.49 0.02 0.33 1.87 0.04 0.13 0.12 0.15 0.13 0.76 2.01 2.40
TE
0.11 0.01 0.03 0.20 2.97 1.17 0.28 3.46 7.30 0.06 0.24 0.08 0.01 0.05 0.09 0.05 0.07 0.05 0.31 0.01 0.01 2.40 0.02 0.02 0.14 0.03 1.01 0.24 0.01 0.59 0.55 0.06 0.55 0.05 0.03 0.24 0.34 0.79 0.40 0.06 0.44 2.97 0.01 0.47 0.11 0.13 0.07 0.78 1.73 2.15
EP
0.07 0.01 0.02 0.23 2.51 0.78 0.42 3.62 9.45 0.03 0.16 0.05 0.01 0.03 0.06 0.11 0.06 0.08 0.37 0.01 0.01 1.92 0.01 0.01 0.15 0.02 1.40 0.24 0.01 0.32 0.66 0.09 0.27 0.05 0.02 0.23 0.27 0.79 0.40 0.19 0.24 1.90 0.01 0.74 0.06 0.12 0.08 0.60 2.07 2.43
CC
Guatemala Guinea Honduras Hungary India Indonesia Ireland Italy Japan Jordan Kazakhstan Kenya Kyrgyz Rep. Latvia Lebanon Libya Lithuania Luxembourg Malaysia Malta Mauritania Mexico Moldova Mongolia Morocco Mozambique Netherlands N. Zealand Niger Nigeria Norway Oman Pakistan Panama Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Saudi Arabia Serbia Slovakia Slovenia South Africa South Korea Spain
25
0.11 0.14 0.63 0.78 0.05 0.39 0.09 1.70 0.50 0.54 5.15 29.50 0.07 0.28 100.00
0.23 0.19 0.96 0.55 0.15 0.69 0.11 1.41 0.33 0.19 3.99 22.76 0.07 0.40 100.00
0.05 0.05 0.17 0.12 0.02 0.99 0.08 1.05 0.94 0.59 1.47 17.40 0.03 0.57 100.00
0.41 0.13 0.91 0.27 0.15 0.37 0.19 0.92 2.79 0.12 3.62 22.46 0.37 0.24 100.00
-0.05 -0.14 -5.33 -6.29 -0.03 -3.55 -0.03 -16.02 -0.05 -0.87 -136.08 -3930.87 0.00 -0.66 -6339.24
-0.67 -0.20 -1.48 -0.44 -0.25 -0.59 -0.31 -1.48 -4.52 -0.20 -5.85 -36.30 -0.60 -0.38 -166.39
0.34 0.65 0.15 0.13 0.73 1.13 0.17 1.19 0.75 0.12 1.02 5.04 0.05 1.42 100.00
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0.07 0.08 0.82 0.88 0.04 0.43 0.08 1.17 0.19 0.44 4.86 26.86 0.05 0.15 100.00
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Sri-Lanka Sudan Sweden Switzerland Tanzania Thailand Tunisia Turkey Ukraine UAE UK US Uruguay Vietnam Total World
26
Table 3: Scenario as per COP 21 Paris agreement using optimal Ramsey tax and GDP share tax and deadweight loss (average 2008-2012) Numbers between parentheses correspond to scenarios Deadweight loss in Scenario (7) Ramsey pricing weights
0.02 0.25 1.77 0.72 0.04 0.09 0.87 0.03 2.35 0.07 2.63 0.33 8.35 0.40 0.05 0.10 0.12 0.04 0.32 0.56 0.10 0.10 0.45 4.82 0.02 6.49 0.52 0.24 0.45 3.84 10.04 0.16 0.04 0.06 0.11 0.06 0.09
0.03 0.07 1.27 0.57 0.04 0.43 0.75 0.10 1.16 0.43 4.02 0.74 10.45 0.46 0.07 0.69 0.02 0.04 2.50 0.54 0.12 0.05 0.47 3.67 0.04 7.56 0.50 0.43 0.24 2.67 6.25 0.06 0.09 0.05 0.04 0.19 0.13
-0.02 -0.27 -23.63 -6.18 -0.01 -0.06 -8.13 -0.01 -88.62 -0.03 -37.18 -0.90 -734.36 -0.79 -0.02 -0.03 -0.02 -0.04 -0.23 -4.49 -0.16 -0.13 -2.39 -186.64 -0.02 -365.66 -3.06 -0.69 -2.47 -168.30 -1080.75 -0.33 -0.02 -0.05 -0.02 -0.03 -0.07
-0.01 -0.02 -0.30 -0.13 -0.01 -0.10 -0.18 -0.02 -0.27 -0.10 -0.94 -0.17 -2.45 -0.11 -0.02 -0.16 0.00 -0.01 -0.59 -0.13 -0.03 -0.01 -0.11 -0.86 -0.01 -1.77 -0.12 -0.10 -0.06 -0.63 -1.47 -0.01 -0.02 -0.01 -0.01 -0.04 -0.03
CC
A
U N A
M D
SC RI PT
Deadweight loss in Scenario (6) GDP weights
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Albania Algeria Australia Austria Bahrain Belarus Belgium Bosnia-Herzegovina Brazil Bulgaria Canada Chile China Colombia Costa Rica Croatia Cuba Cyprus Czech Rep. Denmark Dominican Rep. Ecuador Finland France Gabon Germany Greece Hungary Ireland Italy Japan Kazakhstan Latvia Lebanon Libya Lithuania Luxembourg
(7) Optimal Ramsey Pricing as per COP21
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Country
(6) GDP share as per COP 21
27
-0.21 0.00 -0.24 -0.46 -0.08 -0.08 -0.01 -0.01 -0.07 -0.72 -0.09 -0.01 -0.20 -0.71 -0.06 -0.06 -0.03 -0.05 -0.14 -0.31 -0.47 -0.26 -0.08 -0.11 -0.05 -0.27 -0.04 -1.05 -6.49 -0.11 -23.48
U
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-0.63 0.00 -42.69 -10.37 -0.48 -4.69 -0.04 -0.04 -0.45 -4.86 -2.63 -0.03 -0.32 -23.35 -0.51 -0.03 -0.21 -0.07 -6.31 -67.65 -76.10 -7.25 -8.55 -4.83 -0.05 -21.77 -1.18 -184.90 -5341.16 -0.01 -8526.91
N A
M
0.89 0.01 1.02 1.94 0.33 0.33 0.03 0.05 0.31 3.08 0.39 0.04 0.85 3.01 0.28 0.27 0.14 0.20 0.58 1.32 1.99 1.12 0.34 0.45 0.23 1.13 0.15 4.46 27.66 0.46 100.00
D
0.39 0.01 2.04 1.49 0.25 0.70 0.09 0.05 0.25 0.83 0.42 0.20 0.25 2.02 0.78 0.06 0.13 0.09 0.63 2.20 2.58 0.88 0.93 0.45 0.09 1.25 0.47 5.16 28.52 0.05 100.00
A
CC
EP
TE
Malaysia Malta Mexico Netherlands New Zealand Norway Oman Panama Peru Poland Portugal Qatar Romania Russia Saudi Arabia Serbia Slovakia Slovenia South Africa South Korea Spain Sweden Switzerland Thailand Tunisia Turkey UAE UK US Uruguay World Total
28
Panel a - Deadweight loss according to main scenarios DWL Scenarios
US
China
Russia
EU_28
-17.18
-13.71
-8.21
-3.96
-46.81
-5341.16
-734.36
-1080.75
-23.35
-1035.17
-6.49
-2.45
-1.47
-0.71
-8.37
6 7
-761.81
D
Panel b - Deadweight loss ratio vs. US
N
-36.30
A
5
M
-3930.87
U
-795.38
1
-540.46
Japan
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Table 4: Welfare analysis by macro-regions
Scenarios
US
China
Japan
Russia
EU_28
1
1
0.1375
0.2022
0.0044
0.1938
EP
TE
DWL
1
0.3777
0.2262
0.1091
1.2895
6
1
0.1372
0.2024
0.0043
0.1940
7
1
0.3775
0.2265
0.1094
1.2897
A
CC
5
Note: Rows: Scenarios 1, 5, 6, 7
29
DWL saved Countries
526.75
731.91
Japan
787.17
1079.28
Russia
13.22
22.64
EU_28
718.12
Winners
D
1031.64
6208.47
(39)
8504.24
(54)
-35.75
(66)
-0.75
(8)
0.00
(5)
EP
Losers
0.00
(1)
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Indifferent
U
China
N
5334.67
M
3894.57
TE
US
Scenario 7 vs. 6
A
Scenario 5 vs.1
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Table 5: Dead weight loss saved according to Ramsey pricing
A
Note: Number of countries in bracket.
30
S0161-8938(18)30028-0 https://doi.org/10.1016/j.jpolmod.2018.02.003 JPO 6417
To appear in:
Journal
Received date: Revised date: Accepted date:
12-12-2017 27-1-2018 10-2-2018
of
Policy
Modeling
Please cite this article as: Atalla, Tarek., Bigerna, Simona., Bollino, Carlo Andrea., & Polinori, Paolo., An Alternative Assessment of Global Climate Policies.Journal of Policy Modeling https://doi.org/10.1016/j.jpolmod.2018.02.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
An Alternative Assessment of Global Climate Policies Tarek Atalla, University of London Simona Bigerna, Department of Economics, University of Perugia
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Carlo Andrea Bollino*, Department of Economics, University of Perugia Paolo Polinori, Department of Economics, University of Perugia
*CORRESPONDING AUTHOR: University of Perugia, [email protected]
Abstract
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This paper analyzes alternative pricing schemes for funding investment in climate policies. This paper proposes a new policy scenario, explicitly disentangling the issue of burden sharing of financing new investments from the issue of taxation energy consumption and therefor emissions. We compare traditional allocation schemes with an optimal Ramsey pricing by applying demand elasticity values, derived from empirical estimations of household behavior for the 106 leading countries in the world, representing around 90% of total world energy consumption and carbon emissions in 2014. We calculate country-specific alternative taxation options: uniform, equitable and Ramsey pricing schemes, applied to households, assessing the related welfare effects. Our results show that the optimal pricing scheme, for a given investment need, can improve world welfare at the expenses of equitable considerations. In addition, the aggregate societal benefit outweighs the losses associated with specific group of countries, paving the way for easier political agreement, using compensation schemes to redistribute the proceeds.
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Keywords: demand elasticity; carbon price; heterogeneous consumers’ behavior; optimal Ramsey prices, COP 21
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JEL Category Selection: D11 D12 C10 H21 Q41
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1. Introduction Climate policies ultimate goal is to mitigate global warming and limit the role of climate change as a new type of global externality (Weitzman, 2015, 2016), which is characterized by (i) the
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multifaceted nature due to several pollutants which concur to global warming; ii) the high number of stakeholders involved; iii) the unprecedented degree of coordination and cooperation required to solve such issue.
In the past scholars have mainly focused on carbon pricing as a crucial instrument to achieve emission reductions (Fischer and Morgenstern, 2006; Alberola et al., 2009; Weitzman, 2015),
U
designing policy architectures for abatement purposes (Bréchet and Lussis, 2006; Seidma, and
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Lewis, 2009; Rogelj et al. 2013; Kennedy and Basu, 2014; Han et al., 2015) and also for
A
promotion of new zero or low-carbon technologies (Benz and Trück, 2009; Goel and Hsieh,
M
2006; Newbery, 2016).
Recently, Nordhaus (2015) and Weitzman (2016) have focused the analysis on the hardships
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associated with obtaining a global international agreement because of free-riding behaviors,
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advocating the necessity of a new policy architecture to set up a “World Climate Assembly”.
EP
Since the Kyoto Protocol, the limited success international political meetings (Weitzman, 2015; 2016) has been plagued by bitter confrontation between developed and emerging economies on a
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fair economic allocation of the burden associated with carbon emissions reduction, taking into consideration the heterogeneity of the populations involved (Hassett and Metcalf, 1995; Metcalf
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and Weisbach, 2009). Coordination attempts among countries to cope with climate change failed to reach a politically viable implementation of the classical solution of computing a Pigouvian taxation based on the criterion of adding marginal social damage to the marginal private cost. The aim of this paper is to design an economically optimal carbon price to finance investments in
2
climate policies, which can maximize the likelihood of reaching a political consensus. In this sense, we advocate for a more general energy tax concept that can financially settle the cost for mitigating damages associated with energy usage, rather than a carbon price per se. This paper
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proposes an optimal tax scheme to finance a portfolio of different mitigation policies, which mainly requires new technologies investments and innovation, not only a mere energy demand reduction.
This is the main motivation to support our optimistic claim that such scheme can be politically acceptable: we shift the policy focus from the unique target of reducing or limiting the energy
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consumption to a complementary target of allocating in the most efficient way the burden to
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support new investment in new technologies and actions that will ultimately bring about climate
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change mitigation. In general, a mark-up on top of energy prices to raise such financing involves
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the dilemma of equity vs. efficiency, but we focus only on efficiency through a Ramsey (1927) pricing scheme, which minimizes the deadweight losses (DWL) associated with given market
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inefficiencies. In other words, a Ramsey pricing mechanism has the advantage of maximizing
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efficiency for a given fund raising target.
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Surprisingly, the vast literature on climate policy has not explored this analytical tool as a mechanism for sharing the cost burden of climate policy and for setting an efficient way to
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achieve an international consensus among countries for the promotion of an effective climate change policy (Buchner and Carraro, 2005).
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We contribute to the literature in two ways. First, we make explicit use of households’ preferences, as expressed through their energy demand behavior. This is a more complex, yet more accurate way to quantify the ‘polluters pay’ principle. Households are the final consumers of goods and services and consumption goods incorporate energy used in the production process,
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whether they are produced domestically or imported. In addition, households are the ultimate owners of the corporate sector and the final beneficiary of government expenditures. Accordingly, allocation of a tax burden based on households’ consumption is a more precise
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scheme to account for all of the energy incorporated in the society’s economic activity. Second, we explicitly disentangle the issue of burden sharing of financing new investments from the issue of taxation energy consumption and therefor emissions. We propose a more understandable and transparent tool that allows to implement a more effective global policy that is focusing on new technology investment incentives and related sharing mechanism, and not
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directly on the issue of emission reduction. Indeed, the recent dissatisfactory records of the
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international policy forums call for exploring alternative solutions to be compared with the
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mainstream ones.
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The paper is organized as follows. Section 2 presents a brief review of the related literature. Section 3 presents the policy strategies and briefly describes the methods and data set used.
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Section 4 presents the empirical results and the discussion of the alternative schemes. Section 5
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presents conclusions and policy implications.
2. A discussion of the literature related to Ramsey pricing applications
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Policy makers have long studied opportunities for emissions reductions, spurring a plentiful of literature offering solutions to implement carbon price (see among others: Fischer and
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Morgenstern, 2006; Benz and Trück, 2009; Cooper, 2010; Cramton et al. 2015; Metcalf and Weisbach, 2009; Nordhaus, 2007; 2013; Han et al., 2015 Calel and Dechezleprêtre, 2016)). Operationally, there have been the EU carbon emissions trading system (EU ETS) in 2005, substantially choked by world recession of 2009 (Hu et al, 2015), declaration by China to reduce
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its power sector emissions by 60% by 2020, stop emissions growth by 2030, and cut carbon intensity by 45% based on 2005 levels (UNFCCC, 2016). However, most of the climate policies already implemented are local or regional; global agreements have faced stiff opposition. The
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agreement at COP 21 marks a turning point, but there is still disagreement about the allocation of the financial burden.
Applying the Ramsey pricing mechanism in the energy sector may offer a solution for an efficient allocation, because Ramsey pricing minimizes the welfare loss associated with taxation. This mechanism has been applied in tariff regulations for public utility sectors, such as
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telecommunications, transport, pharmaceuticals, electricity and gas distribution (Laffont and
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Tirole, 1996), oil refining sector Babusiaux and Pierru (2007), optimal California gasoline tax
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proposal by Lin and Prince (2009) and airport operation cost recovery (Hakimov and Mueller,
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2014). Many application of Ramsey pricing have analyzed the heterogeneity of demand elasticity behavior to justify charging differentiated prices to different groups of customers, such as the
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analysis of residential electricity customers in the US (Berry, 2002), China (Qi et al., 2009, Sun
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and Li, 2013), Russia (Nahata et al. 2007), Brazil (Santos et al., 2012), Japan (Matsukawa et al,
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1993) and European countries (Deeney et al., 2016). Most of these studies conclude that the actual tariffs are at variance with the optimal Ramsey
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pricing scheme. More recently, other studies focused on Ramsey pricing to optimally allocate the social cost of externalities, such as the environmental cost of air traffic (Martín-Cejas, 2010), or
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electric network congestion and security management (Bigerna and Bollino, 2016). Van der Ploeg (2016) and Boeters (2014) discuss various options for the optimal carbon taxation.
Three main observations follow from the literature review. First, policy actions tend to impose inefficient price schemes. Typical examples are the different tariff structures for residential and
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industrial electricity users, the various tax rates on gasoline and diesel in the transport sector and the multiple tax rates on electricity and natural gas for residential consumers. These examples raise the question of why policy actions are inefficient and why policy-makers do not use
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Ramsey schemes. Second, Ramsey pricing maximizes efficiency, but does not take into account equity across groups. Typically, poor consumers are less price elastic because they cannot afford flexible behavior, often in the form of more efficient capital stock. This raises questions about the costs of non-efficient solutions and the terms of the equity-efficiency dilemma1.
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Third, implementation of the Ramsey pricing worldwide given that a carbon price regulation is
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needed may prove difficult, even if it is welfare enhancing and good for producers. Regulatory
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actions might face different difficulties due to consumers and technologies heterogeneity.
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However, in practical terms, implementation of Ramsey pricing scheme within countries is more difficult than between countries, as highlighted in studies on investment required for worldwide
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public goods (see among others Danzon and Towse, 2003).
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From a political viewpoint, it would be possible to explain to the population that differential
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pricing may be advantageous and not detrimental to their interest, in case of a positive spur to global incentives to R&D in decarbonization technologies. Furthermore, in poor and emerging
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countries, it could be useful to explain that as income grows and the price elasticity changes, the
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regulatory scheme will be adequate to the actual economic scenario.
3. Policy strategies 3.1 The traditional policy scenarios 1
Additionally, the status-quo tariff structure is the result of historical lobbying by different constituencies. The strongest constituencies may oppose tariff changes and it may be politically difficult to find political legitimacy for changing the tariff structure based on innovative empirical findings.
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In order to directly penalize carbon emissions, the policy-driven carbon price has to be different from the private marginal costs, pointing to the classical solution for solving market distortions based on the equi-marginal principle. This solution remains short of providing efficiency
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financing for other climate policies. The aim of fostering renewable energy, its least cost deployment, energy efficiency measures, research and other innovative technologies (e.g., carbon capture and storage) needs to be financed. This is also complemented by the uncertainty about the adequate measuring of the price of the social cost of carbon (Tol, 2011).
The approach used so far involves designing the individual country commitments in proportion
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to emissions or GDP, valued at a common marginal price. This is undoubtedly the optimal
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solution, for carbon pricing mainly aims to (fully) internalize the social costs of carbon rather
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than generate revenue for funding emission mitigation technology. However, in some economic
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circles, this could be considered as a proxy trade barrier. The main shortcoming of this approach is that it creates a burden for newly industrialized countries that produce goods ultimately
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consumed by advanced economies. In other words, energy intensive manufacturing countries risk
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(Gasim (2015).
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to be penalized for the carbon content of the final goods consumed by higher income economies
Consequently, the question is in finding the optimal way of achieving a worldwide win-win
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solution, given that strong international governance is needed, because the specific country taxation needs to be imposed or agreed upon in a supra-national context such as COP 21
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(Buchner and Carraro, 2005; Bollino and Micheli, 2014).
3.2 The new policy proposal We propose a new policy strategy that can be used to allocate a financial burden among world countries to support any investment requirement at the world scale. We assume that
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heterogeneous consumers value the marginal damage of greenhouse gas emissions differently, due to differences in interest, perception income and values across the world 2 and we clearly separate the issue of designing a complex carbon pricing policy that reduces energy consumption
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(and therefore emission) from designing an operational method for allocating financing among countries.
The new policy computes each country’s share of the cost of climate change mitigation, independent of how that overall cost is calculated. This policy proposal can be applied to finance two main investment policies, which are now under discussion in the international agenda: 1) the
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consensus cost estimate of the level of investment necessary to achieve the 450 ppm Scenario,
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according to the New Policies Scenario developed by the International Energy Agency (IEA,
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2016) by 2035; 2) the COP21 agreement to provide financial aid to poorer economies by 2020.
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The consensus quantification of the investment cost of the New Policies Scenario is around 1.5% of world GDP. The COP21 agreement reached in Paris on December 2015 (United Nations,
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2015) been confirmed recently by the conclusion of the COP23 in Bonn in November 2017. The
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agreement quantifies an explicit target for financial aid and will enter into effect once ratified by
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55 countries that represent at least 55% of GHG emissions. The agreement stipulates financial aid of at least USD $100 Billion to poorer economies by 2020, a sum set to potentially increase
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in the future. However, the agreement does not describe a path forward on the allocation of donor country contributions, nor which countries will receive financial aid. Many possible
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combinations of the 195 UNFCCC3 participating member countries could bring the agreement
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For example, since carbon emissions are of greater concern to children (given that they are likely to live long enough to see greater impact from climate change), an older society, such as Europe, is likely to have a different perception than a younger society, like some emerging countries where half of the population is less than 16 years old. 3 The UNFCCC stands for the United Nations Framework Convention on Climate Change
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into effect. We consider a plausible agreement that includes the top emitting countries and the richest countries in terms of GDP per capita, representing at least 55% of total emissions.
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3.2. Methods and Data We analyze different pricing schemes, which are: a uniform price taxation, levied in proportion to the country’s importance in terms of world GDP and/or carbon emissions; an efficient Ramsey pricing scheme; an equitable pricing scheme, levied only on the richest subset of countries.
We construct data for the household sector by considering the final consumption expenditure
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composed of two goods, energy consumption and other goods consumption, for 106 countries for
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the period 2000 to 2013. Quantities of energy consumption (in Ton of Oil Equivalent, Toe)
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include both energy for residential uses and energy for transportation, representing the actual
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direct expenditure of households for all energy uses (in constant 2005 US dollars). This allows us to capture the households’ preferences for direct energy use for all households needs.
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We model aggregate consumption demand of the household sector, assuming cost minimization
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behavior of the individual heterogeneous agent. Our hypothesis considers each country as a
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representative agent that rationally optimizes simultaneous choice of a bundle of goods, which is based on the aggregation of heterogeneous agents (Deaton and Muellbauer, 1980) within each
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country. Empirical estimation (available in detail in Atalla et al. 2017) allow to evaluate t household price elasticity of energy consumption, which is directly related to emissions. The
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elasticity is a revealed preference measure of the willingness to pay for energy consumption, and indirectly, for the willingness to pay for emissions reductions. The inverse of the demand elasticity is used to compute the Ramsey proportionality factor to compute the burden sharing of
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the world climate policy for each country. The DWL associated with the Ramsey solution are compared to other traditional burden sharing mechanisms.
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4. Empirical results and discussion on alternative carbon pricing options It is increasingly accepted that COP 21 agreement will be not be alone sufficient in getting climate change mitigation. Indeed, if the aim is to keep global warming below 2° C over the long run, COP 21 allows to achieve only half of the emission reduction needed. However, this goal can be reached with additional mitigation policies (Vandyck et al., 2016).
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We design alternative carbon price taxation options, taking as a constraint the amount needed
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worldwide for climate policy. The latest IEA Scenarios (IEA, 2016) envision USD $100 billion
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of investment per year as necessary to support mitigation policy to stabilize atmospheric CO2
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concentration at 450 ppm by 2030. In every scenario, we compute the allocation of this investment among countries according to alternative taxation options. The resource constraint is
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as follows: we consider that the amount of USD $100 billion (in real 2005 dollars) is on average
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0.2% of world GDP, 0.3% of total household expenditure worldwide, or 2.5% of total household
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energy expenditure worldwide.
We take the viewpoint of the international group of countries who undersign COP21 Agreement
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interested in a political consensus to promote climate policy based on two pillars: global efficiency and clear distributional commitment among countries.
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4.1. Preliminary results We construct seven alternative scenarios of energy price taxation on the household sector in each country (Table 1). In each scenario, we introduce a surcharge on the existing energy taxation. [Table 1]
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The taxation revenue worldwide is constrained by the policy target in all Scenarios. The first five Scenarios are designed to encompass global policy financing.
The last two
Scenarios consider only the donor policy to the poorer countries. We report the detailed results
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for all Scenarios in Tables 2 and 3. [Table 2] [Table 3]
In detail, Scenario 1 designs a taxation burden for each country that is proportional to each country’s share of world GDP. In Scenario 2, the allocation burden is proportional to each
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country’s total household consumption expenditure and in Scenario 3 the allocation burden is
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proportional to each country’s household expenditure on energy. This Scenario imposes the
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equimarginal principle. In scenario 4, the allocation burden is proportional to each country
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carbon emissions. Each of these four Scenarios imposes a taxation burden proportional to a measure of the size of each country as part of the world total. Scenario 5 is the optimal Ramsey
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pricing scheme based on the estimated price elasticities. We compute optimal Ramsey prices for
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every country using the inverse of the absolute value of the energy demand elasticity (Column 5
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of Table 2).
Scenarios 6 and 7 consider only the top countries in term of emissions and of GDP per capita, in
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the spirit of the recent Paris COP21 agreement. We identify a group of at least 55 countries that represent at least 55% of the world emissions, selected according to the highest GDP per capita.
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In this way, we identify the top 67 richest countries, as listed in table 3. In particular, in Scenario 6 we compute the burden share of participating countries using the GDP shares and in Scenario 7 we use the optimal Ramsey shares.
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For all Scenarios, we compute the DWL in each country associated with different taxation schemes. The DWL is the area under the energy demand function for each Scenario between the pre- and post-taxation price of energy, after the imposition of the taxation surcharge.
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Scenarios 1 through 4 are variants of the proportional principle, where each country shares the world burden in proportion to the size of its GDP, household consumption expenditure, household energy expenditures and carbon emissions, respectively. This implies that the weight of each country in total world wellbeing forms the basis for its contribution to climate policy cost.
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On the other hand, Scenario 6 is founded on household behavior, as reflected in energy price
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elasticity, to minimize welfare losses resulting from the implemented policy action. In this case,
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each country’s contribution is based on its human and economic behavioral decision making,
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irrespective of macro-economic variables such as GDP or carbon emissions. The optimal taxation regime based on this scenario may lead to inequalities, as poorer developing countries
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that use outdated and inefficient physical capital, and thus have lower demand elasticity, may
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end up being taxed a higher portion of their income. Equation 5 reflects this. An interesting result
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from Scenario 5 is that optimal taxation would impose a lower burden on leading polluters such as US, Japan, Brazil, UK, France and Italy and a higher burden on China, India and Russia.
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We report the results of Scenarios 6 and 7, in which the 67 richest countries, as measured by GDP per capita, share the burden of climate policy in Table 3. This group includes China, which
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has the lowest GDP per capita in the group but is the largest emitter. In total, this subset of 67 countries represents around 80% of 2014 global carbon emissions. Both scenarios require a large threshold of countries to account for the constraint of the emissions requirements, especially when compared to Scenario 1.
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The burdens for each country vary among the scenarios. In Scenario 1, the US and China have a share of 29.5% and 7.8%, respectively. These values are 22.8% and 9.6% in Scenario 3, respectively. These results show that China has to pay more in the equimarginal case than in the
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GDP share case. The values for these countries decrease to 22% and 6% in Scenario 5; 28% and 8% in Scenario 6 and 27% and 10% in Scenario 7, respectively. These results show that China’s tax burden varies considerably, and more so when using the Ramsey scheme in Scenario 7 compared to Scenario 5. This shows that China must pay a high price to participate to the club of the richest countries, and thus the group of top donors, in the efficient Ramsey taxes allocation.
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4.2. Welfare analysis
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We report the DWL associated with scenarios 1 and 5 in Table 2, while DWL for scenarios 6 and
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7 are reported in table 3. Not surprisingly, the loss associated with Ramsey pricing is the least, as
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the methodology’s goal is to reduce economic inefficiencies. A proportional carbon tax in all countries yields a DWL around five times larger, while an equitable scheme that charges higher
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tax shares according to GDP yields an even greater loss.
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These elements are assessed in further details below. Table 4 shows the DWL associated to
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different scenarios for the main five macro-regions (Panel a). According to the proportional principle, the US contribute with higher welfare loss regardless of the number of countries
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considered. On average (Panel b) the losses of other macro-regions lie between 0,04% and 21%
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of US DWL.
[Table 4]
Using the Ramsey principle EU28 becomes the macro-region with the higher DWL while the US moves to the second position. Ramsey method not only consistently reduces the inefficiency
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associated to the proportional principle but it changes the relative positions in term of macroregions’ burdens also reducing the disparities among macro-regions. Inefficiency reduction due to Ramsey pricing is an important driver to free resources.
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Considering the amount of DWL saved for each macro-region (Table 5), it is interesting to highlight that the amount saved is noticeable greater than the aggregate DWL lost by the losers both considering all the countries or the subgroup of top countries in term of emissions and of GDP per capita. [Table 5]
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Considering all the individual countries, only one of the top 10 winner countries could be
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sufficient (Figure 1) to compensate the aggregate DWL of the all 66 losers. Indeed, the aggregate
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welfare measure is equal to 35.75 that is lower than the India DWL avoided (49.42).
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[Figure 1]
Consequently, the contribution supported by the losers would be largely compensated by the
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efficiency gains obtained by the richer and heavy-polluting countries. In other words, the
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Ramsey pricing mechanism can maximize the likelihood of reaching a political consensus on
abatement.
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how to foster and finance the technological change in order to reduce the long-run cost of
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Results do not change taking into account scenarios 6 and 7, which consider only the donor policy to the poorer countries. In this case the additional aggregate DWL should also include the
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DWL associated to the equi-proportional principle for the poorest countries (scenario 1) and the DWL due to the Ramsey pricing for the new losers that arise in scenarios 6 and 7. This means that the DWL that should be compensated amounts to 47.05, (35.75 + 10.55 + 0.75),
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respectively. Also in this case the DWL saved by the richest countries is more than sufficient to compensate the welfare reduction that affects poorest countries (Figure 2). [Figure 2]
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Indeed, the last of the top ten countries, South Korea, avoids more than 50% of compensation required by all the other countries: poorest and new losers. This confirms that it could be possible to achieve a worldwide win-win solution with differential carbon pricing to support low zerocarbon technologies pursuing an efficiency policy.
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5. Conclusion and Policy Implications
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This paper has proposed a new policy to finance climate change mitigation actions, such as
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fostering renewable energy, its least cost deployment, energy efficiency measures, research and
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other innovative technologies. Alternative pricing schemes aimed at funding investment in climate policies have been compared: traditional allocation schemes with an optimal Ramsey
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pricing by applying a complete demand system for the world households’ consumption behavior
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and used the resulting country price elasticity values to compute an optimal Ramsey price
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scheme to finance climate policy.
Empirical results lead to two implications. First, the new policy can provide to policy makers a
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quantitative assessment of the relative efficiency of different policy actions. Our proposal shows an efficient worldwide taxation scheme to fund investments to spur climate policy actions. The
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taxation strategy depends crucially on the heterogeneity of household behavior in countries around the world. Therefore, policy actions could be designed around the efficiency principle, with eventual compensations for political reasons, rather than on a debatable equity principle that leads to greater economic inefficiency.
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Second, our empirical estimation shows significant differences in the burden allocation when the allocation impinges only on the world’s richest countries. Our optimal pricing scheme applied to the group of richest countries shows that some countries could pay a high “access price” to
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adhere to this group of richest countries. Therefore, we warn about the need, through political negotiations, to assess the risk of opportunistic behavior by countries attempting to avoid this high price. Furthermore, the combined global societal benefit outpaces the resulting losses associated with some countries which opens the door for potential compensation through a redistribution of proceeds.
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The issue of raising taxation is a difficult task for the policy maker, because it involves inevitably
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distortions. In general, the policy maker is confronted with funding limitations and the associated
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efficiency-equity trade-off. Equitable pricing policies have a role in improving the living
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conditions of poor households, but usually impose a societal cost in terms of market inefficiency. On the contrary, when implementing policies to promote maximum economic efficiency, the
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poorest in the society often get hurt.
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In this respect, the new policy proposal can show to the policy maker the quantitative range of
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the associated efficiency-equity trade-off, spanning from one extreme of maximum efficiency to the other extreme of maximum equity. The estimations of demand elasticities are useful to
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construct the minimum distortion pricing policy (the so-called Ramsey pricing), the other extreme being the maximum equitable solution, based on countries relative importance, in terms
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of the share of total world GDP and/or household expenditure and/or carbon emissions (the equimarginal principle).
On another level, the same policy maker needs to assess the economic impact associated with equitable intervention, like price subsidy to the poor and elderly. We provide such measure in
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terms of DWL. This result suggests that governments should base their policies more on the behavior of their populations rather than abstract technical standards defined by bureaucrats. In conclusion, this paper advocates a new policy stance worldwide, using the estimated consumer
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demand elasticity as a novel basis to differentiate taxation worldwide to finance global policies aimed at climate change mitigation. Policy makers should be aware that they will face politically responsible economic agents who need efficient and welfare-improving proposals to pay for
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investments to mitigate climate change.
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4500 4000
3894.57
3500 3000 2500 1500 787.17
1000
526.75
500
259.19 132.54 130.23 120.36 63.7
53.39 49.42
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0
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2000
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Figure 1: Dead weight loss avoided comparing scenario 1 vs. 5; top ten countries
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6000 5334.67
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5000 4000
2000 1000
1079.28
731.91
363.89 185.78 183.85 167.67 88.35 75.63 67.34
A
CC
0
EP
3000
Figure 2: Dead weight loss avoided comparing scenario 6 vs. 7; top ten countries
22
Table 1: Description of various scenario implemented Scenario
Description Allocation based on GDP shares
2
Allocation based on Household Consumption Expenditure shares
3
Allocation based on Household Energy Expenditure shares
4
Allocation based on Carbon emissions shares
5
Allocation based on Ramsey optimal pricing
6
Allocation based on the GDP shares of top 68 countries as per COP21
7
Allocation based on Ramsey optimal pricing of top 68 countries as per COP21
A
CC
EP
TE
D
M
A
N
U
SC RI PT
1
23
Table 2: Alternative taxation options –allocation shares by countries worldwide -GDP and population shares (average 2008-2012). Numbers between parentheses correspond to scenarios (1) GDP
(2) Total Expend
(3) Energy Expend
(4) Carbon Emission
(5) Optimal Ramsey
Deadweight loss in Scenario (1)
Deadweight loss in Scenario (5)
Population share
Albania Algeria Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Bolivia Bosnia-Herz. Brazil Bulgaria Burkina Faso Cambodia Cameroon Canada Chile China Colombia Congo DR Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Rep. Denmark Dom. Rep. Ecuador Egypt El Salvador Ethiopia Finland France Gabon Gambia Georgia Germany Ghana Greece
0.02 0.24 0.01 1.66 0.68 0.05 0.04 0.16 0.09 0.82 0.02 0.03 2.21 0.07 0.01 0.02 0.04 2.48 0.31 7.87 0.38 0.02 0.05 0.04 0.10 0.11 0.04 0.31 0.53 0.09 0.09 0.25 0.00 0.04 0.42 4.54 0.02 0.00 0.02 6.11 0.04 0.49
0.03 0.13 0.02 1.67 0.56 0.05 0.03 0.23 0.20 0.66 0.03 0.04 2.80 0.10 0.01 0.02 0.05 2.33 0.36 5.18 0.45 0.08 0.07 0.05 0.10 0.09 0.04 0.22 0.42 0.14 0.14 0.42 0.06 0.11 0.36 4.06 0.01 0.00 0.03 5.13 0.11 0.54
0.04 0.08 0.01 1.30 0.70 0.04 0.02 0.08 0.16 0.83 0.04 0.04 2.07 0.14 0.10 0.08 0.20 2.38 0.45 9.57 0.30 0.26 0.06 0.18 0.14 0.02 0.06 0.42 0.66 0.14 0.08 0.12 0.07 0.30 0.50 3.76 0.06 0.01 0.03 6.52 0.38 0.54
0.02 0.40 0.02 1.21 0.21 0.11 0.08 0.19 0.21 0.32 0.05 0.08 1.44 0.16 0.01 0.01 0.02 1.59 0.26 29.57 0.24 0.01 0.03 0.02 0.07 0.12 0.02 0.36 0.13 0.07 0.12 0.72 0.02 0.02 0.18 1.11 0.01 0.04 0.03 2.39 0.03 0.28
0.02 0.05 0.00 1.03 0.46 0.01 0.04 0.09 0.35 0.61 0.03 0.08 0.94 0.35 0.05 0.06 0.36 3.26 0.60 5.55 0.37 0.40 0.06 0.83 0.56 0.01 0.03 2.03 0.44 0.10 0.04 0.09 0.04 0.74 0.38 2.98 0.03 0.00 0.01 6.14 0.38 0.41
-0.01 -0.20 0.00 -17.39 -4.55 -0.04 0.00 -0.07 -0.04 -5.99 -0.01 0.00 -65.22 -0.02 -0.02 -0.01 -0.03 -27.36 -0.66 -540.46 -0.58 -0.02 -0.02 -0.01 -0.02 -0.01 -0.03 -0.17 -3.30 -0.12 -0.09 -0.25 0.00 -0.03 -1.76 -137.36 -0.01 0.00 -0.01 -269.11 -0.09 -2.25
-0.03 -0.09 -0.01 -1.66 -0.75 -0.02 -0.06 -0.14 -0.57 -0.99 -0.05 -0.13 -1.52 -0.56 -0.08 -0.10 -0.59 -5.28 -0.97 -13.71 -0.60 -0.65 -0.10 -1.34 -0.90 -0.02 -0.05 -3.28 -0.71 -0.16 -0.07 -0.14 -0.07 -1.20 -0.61 -4.82 -0.06 0.00 -0.02 -9.92 -0.61 -0.66
0.05 0.58 0.05 0.36 0.14 0.14 0.02 2.43 0.15 0.18 0.16 0.06 3.18 0.12 0.26 0.23 0.32 0.56 0.28 21.81 0.75 1.06 0.07 0.32 0.07 0.18 0.01 0.17 0.09 0.16 0.24 1.32 0.10 1.35 0.09 1.06 0.02 0.03 0.07 1.33 0.41 0.18
U
N
A
M
D
TE
EP
CC
A
SC RI PT
Country
24
A
N
-0.40 -0.18 -0.05 -0.57 -4.49 -1.67 -0.31 -3.50 -8.21 -0.07 -0.08 -4.04 -0.02 -0.12 -0.06 -0.06 -0.24 -0.17 -1.17 -0.01 -0.03 -1.34 -0.02 -0.02 -0.13 -0.43 -2.55 -0.43 -0.14 -8.72 -0.43 -0.04 -2.33 -0.07 -0.09 -0.41 -1.25 -4.05 -0.51 -0.05 -1.12 -3.96 -0.04 -0.36 -0.36 -0.19 -0.27 -0.76 -1.73 -2.62
0.23 0.16 0.12 0.16 19.41 3.91 0.07 0.99 2.08 0.10 0.26 0.67 0.09 0.04 0.07 0.10 0.05 0.01 0.46 0.01 0.06 1.85 0.06 0.04 0.52 0.38 0.27 0.07 0.25 2.58 0.08 0.05 2.83 0.06 0.11 0.47 1.52 0.63 0.17 0.03 0.35 2.32 0.17 0.44 0.12 0.09 0.03 0.81 0.81 0.75
SC RI PT
-0.17 0.00 -0.02 -0.51 -53.91 -5.73 -1.81 -123.86 -795.38 -0.03 -0.24 -0.04 0.00 -0.02 -0.03 -0.02 -0.02 -0.05 -0.46 0.00 0.00 -31.42 0.00 0.00 -0.43 -0.01 -7.63 -0.36 0.00 -1.06 -3.45 -0.03 -0.22 -0.03 0.00 -0.33 -0.63 -3.57 -1.93 -0.02 -0.23 -17.18 0.00 -0.37 -0.02 -0.15 -0.05 -4.65 -49.79 -56.01
U
0.25 0.11 0.03 0.35 2.78 1.03 0.19 2.16 5.08 0.04 0.05 2.50 0.01 0.07 0.04 0.03 0.15 0.11 0.73 0.01 0.02 0.83 0.01 0.01 0.08 0.27 1.58 0.26 0.09 5.40 0.27 0.02 1.44 0.04 0.06 0.25 0.77 2.50 0.32 0.03 0.69 2.45 0.03 0.22 0.22 0.12 0.16 0.47 1.07 1.62
A
0.04 0.01 0.03 0.16 6.80 1.85 0.12 1.30 3.89 0.07 0.86 0.04 0.02 0.03 0.07 0.13 0.05 0.04 0.74 0.01 0.01 1.53 0.02 0.06 0.19 0.01 0.55 0.10 0.00 0.29 0.15 0.21 0.54 0.03 0.02 0.17 0.27 1.04 0.16 0.28 0.28 5.93 0.00 1.71 0.16 0.11 0.05 1.56 1.93 0.89
M
D
0.32 0.06 0.07 0.35 3.05 1.08 0.36 3.41 8.20 0.07 0.11 0.59 0.02 0.07 0.06 0.03 0.10 0.10 0.38 0.01 0.02 1.46 0.02 0.02 0.19 0.12 1.16 0.25 0.05 1.67 0.47 0.04 0.43 0.06 0.04 0.24 0.53 1.34 0.49 0.02 0.33 1.87 0.04 0.13 0.12 0.15 0.13 0.76 2.01 2.40
TE
0.11 0.01 0.03 0.20 2.97 1.17 0.28 3.46 7.30 0.06 0.24 0.08 0.01 0.05 0.09 0.05 0.07 0.05 0.31 0.01 0.01 2.40 0.02 0.02 0.14 0.03 1.01 0.24 0.01 0.59 0.55 0.06 0.55 0.05 0.03 0.24 0.34 0.79 0.40 0.06 0.44 2.97 0.01 0.47 0.11 0.13 0.07 0.78 1.73 2.15
EP
0.07 0.01 0.02 0.23 2.51 0.78 0.42 3.62 9.45 0.03 0.16 0.05 0.01 0.03 0.06 0.11 0.06 0.08 0.37 0.01 0.01 1.92 0.01 0.01 0.15 0.02 1.40 0.24 0.01 0.32 0.66 0.09 0.27 0.05 0.02 0.23 0.27 0.79 0.40 0.19 0.24 1.90 0.01 0.74 0.06 0.12 0.08 0.60 2.07 2.43
CC
Guatemala Guinea Honduras Hungary India Indonesia Ireland Italy Japan Jordan Kazakhstan Kenya Kyrgyz Rep. Latvia Lebanon Libya Lithuania Luxembourg Malaysia Malta Mauritania Mexico Moldova Mongolia Morocco Mozambique Netherlands N. Zealand Niger Nigeria Norway Oman Pakistan Panama Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Saudi Arabia Serbia Slovakia Slovenia South Africa South Korea Spain
25
0.11 0.14 0.63 0.78 0.05 0.39 0.09 1.70 0.50 0.54 5.15 29.50 0.07 0.28 100.00
0.23 0.19 0.96 0.55 0.15 0.69 0.11 1.41 0.33 0.19 3.99 22.76 0.07 0.40 100.00
0.05 0.05 0.17 0.12 0.02 0.99 0.08 1.05 0.94 0.59 1.47 17.40 0.03 0.57 100.00
0.41 0.13 0.91 0.27 0.15 0.37 0.19 0.92 2.79 0.12 3.62 22.46 0.37 0.24 100.00
-0.05 -0.14 -5.33 -6.29 -0.03 -3.55 -0.03 -16.02 -0.05 -0.87 -136.08 -3930.87 0.00 -0.66 -6339.24
-0.67 -0.20 -1.48 -0.44 -0.25 -0.59 -0.31 -1.48 -4.52 -0.20 -5.85 -36.30 -0.60 -0.38 -166.39
0.34 0.65 0.15 0.13 0.73 1.13 0.17 1.19 0.75 0.12 1.02 5.04 0.05 1.42 100.00
SC RI PT
0.07 0.08 0.82 0.88 0.04 0.43 0.08 1.17 0.19 0.44 4.86 26.86 0.05 0.15 100.00
A
CC
EP
TE
D
M
A
N
U
Sri-Lanka Sudan Sweden Switzerland Tanzania Thailand Tunisia Turkey Ukraine UAE UK US Uruguay Vietnam Total World
26
Table 3: Scenario as per COP 21 Paris agreement using optimal Ramsey tax and GDP share tax and deadweight loss (average 2008-2012) Numbers between parentheses correspond to scenarios Deadweight loss in Scenario (7) Ramsey pricing weights
0.02 0.25 1.77 0.72 0.04 0.09 0.87 0.03 2.35 0.07 2.63 0.33 8.35 0.40 0.05 0.10 0.12 0.04 0.32 0.56 0.10 0.10 0.45 4.82 0.02 6.49 0.52 0.24 0.45 3.84 10.04 0.16 0.04 0.06 0.11 0.06 0.09
0.03 0.07 1.27 0.57 0.04 0.43 0.75 0.10 1.16 0.43 4.02 0.74 10.45 0.46 0.07 0.69 0.02 0.04 2.50 0.54 0.12 0.05 0.47 3.67 0.04 7.56 0.50 0.43 0.24 2.67 6.25 0.06 0.09 0.05 0.04 0.19 0.13
-0.02 -0.27 -23.63 -6.18 -0.01 -0.06 -8.13 -0.01 -88.62 -0.03 -37.18 -0.90 -734.36 -0.79 -0.02 -0.03 -0.02 -0.04 -0.23 -4.49 -0.16 -0.13 -2.39 -186.64 -0.02 -365.66 -3.06 -0.69 -2.47 -168.30 -1080.75 -0.33 -0.02 -0.05 -0.02 -0.03 -0.07
-0.01 -0.02 -0.30 -0.13 -0.01 -0.10 -0.18 -0.02 -0.27 -0.10 -0.94 -0.17 -2.45 -0.11 -0.02 -0.16 0.00 -0.01 -0.59 -0.13 -0.03 -0.01 -0.11 -0.86 -0.01 -1.77 -0.12 -0.10 -0.06 -0.63 -1.47 -0.01 -0.02 -0.01 -0.01 -0.04 -0.03
CC
A
U N A
M D
SC RI PT
Deadweight loss in Scenario (6) GDP weights
EP
Albania Algeria Australia Austria Bahrain Belarus Belgium Bosnia-Herzegovina Brazil Bulgaria Canada Chile China Colombia Costa Rica Croatia Cuba Cyprus Czech Rep. Denmark Dominican Rep. Ecuador Finland France Gabon Germany Greece Hungary Ireland Italy Japan Kazakhstan Latvia Lebanon Libya Lithuania Luxembourg
(7) Optimal Ramsey Pricing as per COP21
TE
Country
(6) GDP share as per COP 21
27
-0.21 0.00 -0.24 -0.46 -0.08 -0.08 -0.01 -0.01 -0.07 -0.72 -0.09 -0.01 -0.20 -0.71 -0.06 -0.06 -0.03 -0.05 -0.14 -0.31 -0.47 -0.26 -0.08 -0.11 -0.05 -0.27 -0.04 -1.05 -6.49 -0.11 -23.48
U
SC RI PT
-0.63 0.00 -42.69 -10.37 -0.48 -4.69 -0.04 -0.04 -0.45 -4.86 -2.63 -0.03 -0.32 -23.35 -0.51 -0.03 -0.21 -0.07 -6.31 -67.65 -76.10 -7.25 -8.55 -4.83 -0.05 -21.77 -1.18 -184.90 -5341.16 -0.01 -8526.91
N A
M
0.89 0.01 1.02 1.94 0.33 0.33 0.03 0.05 0.31 3.08 0.39 0.04 0.85 3.01 0.28 0.27 0.14 0.20 0.58 1.32 1.99 1.12 0.34 0.45 0.23 1.13 0.15 4.46 27.66 0.46 100.00
D
0.39 0.01 2.04 1.49 0.25 0.70 0.09 0.05 0.25 0.83 0.42 0.20 0.25 2.02 0.78 0.06 0.13 0.09 0.63 2.20 2.58 0.88 0.93 0.45 0.09 1.25 0.47 5.16 28.52 0.05 100.00
A
CC
EP
TE
Malaysia Malta Mexico Netherlands New Zealand Norway Oman Panama Peru Poland Portugal Qatar Romania Russia Saudi Arabia Serbia Slovakia Slovenia South Africa South Korea Spain Sweden Switzerland Thailand Tunisia Turkey UAE UK US Uruguay World Total
28
Panel a - Deadweight loss according to main scenarios DWL Scenarios
US
China
Russia
EU_28
-17.18
-13.71
-8.21
-3.96
-46.81
-5341.16
-734.36
-1080.75
-23.35
-1035.17
-6.49
-2.45
-1.47
-0.71
-8.37
6 7
-761.81
D
Panel b - Deadweight loss ratio vs. US
N
-36.30
A
5
M
-3930.87
U
-795.38
1
-540.46
Japan
SC RI PT
Table 4: Welfare analysis by macro-regions
Scenarios
US
China
Japan
Russia
EU_28
1
1
0.1375
0.2022
0.0044
0.1938
EP
TE
DWL
1
0.3777
0.2262
0.1091
1.2895
6
1
0.1372
0.2024
0.0043
0.1940
7
1
0.3775
0.2265
0.1094
1.2897
A
CC
5
Note: Rows: Scenarios 1, 5, 6, 7
29
DWL saved Countries
526.75
731.91
Japan
787.17
1079.28
Russia
13.22
22.64
EU_28
718.12
Winners
D
1031.64
6208.47
(39)
8504.24
(54)
-35.75
(66)
-0.75
(8)
0.00
(5)
EP
Losers
0.00
(1)
CC
Indifferent
U
China
N
5334.67
M
3894.57
TE
US
Scenario 7 vs. 6
A
Scenario 5 vs.1
SC RI PT
Table 5: Dead weight loss saved according to Ramsey pricing
A
Note: Number of countries in bracket.
30