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Trabajo Fin de Máster Kyoto and Ma Equilibrium (CGE) analysis of Spanish greenhouse gas 1 Trabajo Fin de Máster Kyoto and Ma ñ ana : A Computable General Equilibrium (CGE) analysis of Spanish greenhouse gas targets to 2020 Autor Michael Bourne Director/es Maria-Luisa Feijoo Bello Facultad de Economía y Empresa 2013 ana : A Computable General Equilibrium (CGE) analysis of Spanish greenhouse gas 2 Kyoto and mañana: A Computable General Equilibrium (CGE) analysis of Spanish Greenhouse Gas targets to 2020 1. Introduction The necessity for international cooperation in conceiving a global strategy to both mitigate and adapt to climate change, coupled with the absence of a sovereign international authority, bestowed upon individual governing bodies world-wide a sense of collective responsibility to engender binding and effectual policy measures. Against this background, the United Nations Framework Convention on Climate Change (UNFCCC) was created, which in turn oversaw the ratification of the Kyoto Protocol (UNFCCC, 1998). This international accord set a detailed roadmap for curbing carbon dioxide (CO2) emissions, as well as a collective basket of non-CO2 ‘greenhouse gas’ (GHG) emissions. 1 More recently, the European Union has taken the lead in fighting climate change, by agreeing a series of further unilateral emissions cuts over the 20132020 period, under the auspices of its Climate and Energy Package (CEP) 2 . Amid discussions on the best way to achieve these goals, the European Union (EU) Emissions Trading Scheme (ETS) emerged for a test period in 2005-2007 and thereafter for different commitment phases from 2008-2028 (European Parliament, 2003; 2004; 2008; 2009a). The ETS created an internal trading market for CO2 emissions permits, initially allocated across a select grouping of sectors (excluding agriculture), with the intention that abatement be incentivised via charges for exceeding (gradually contracting) domestic emissions limits or revenues to more efficient firms from the sale of excess permit allocations. Individual member states distribute emissions permits subject to both the approval of the European Commission and those limits stipulated within the National Allocation Plan (NAP) 3 . When Kyoto expires, the ETS will continue to operate to extend CO2 emissions reductions to 2020 (see Table 1). For non-ETS GHG emissions, parallel EU-wide emissions reductions are implemented up to 2012, although under a ‘burden sharing agreement’ Spain has been granted a softer emissions reduction target (see Table 1). Notwithstanding, in light of Spain’s impressive growth between 1990-2007, some commentators estimate that its economy still faces relatively steep emissions reductions in order to meet its Kyoto commitment (Labandeira and Rodríguez, 2010; González1 The non-CO2 gases within the remit of Kyoto are: methane (CH 4 ), nitrous oxide (N 2 O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF 6 ). Importantly, these gases have a considerably higher Global Warming Potential (GWP) than CO2. 2 see http://ec.europa.eu/clima/policies/package/index_en.htm 3 see http://ec.europa.eu/clima/policies/ets/pre2013/nap/index_en.htm 3 Eguino, 2011). 4 In the post-Kyoto period an independent ‘diffuse’ sector (includes agriculture) emissions target is in place up to 2020 (see Table 1). 5 A cursory examination of Spanish emissions data reveals that diffuse emissions make up 55% of all Spanish GHG emissions, of which the transport sector produces the largest proportion (accounting for more than 40% of total energy consumed in Spain) followed by the agriculture sector which itself accounts for 14% of total Spanish GHG emissions (UNFCCC, 2011). A closer look at Spain’s agricultural emissions reveals that methane emissions from livestock activities constitute the largest proportion of total agricultural emissions (38%), followed by nitrous oxide from fertiliser application (34%), and carbon dioxide from petroleum usage (16%). The remaining emissions are largely nitrous oxide from manure, and small amounts of methane released during field burning in the cereals sectors. Computable general equilibrium (CGE) representations can be employed to quantify the impact of climate change policies because of their ability to assess the interactions between many different agents and sectors across the whole economy. Unlike ‘bottoms-up’ engineering models, CGE ‘top-down’ mathematical models are able to simulate the complex linkages between the direct and indirect consequences of modeller-specified policy shocks, producing as an output a comprehensive representation (i.e., prices, outputs, costs) of the economy-wide impacts. This characteristic is particularly pertinent when examining the integrated nature of energy production and usage across industries and consumers, as well as macroeconomic impacts of policy mandated emissions targets. The adaptability of CGE modelling has led to a range of climate change studies with varying focal points and objectives. In surveying the existing literature we observe multi-region studies (e.g. Böhringer and Rutherford, 2010), whilst differences in the decomposition of emissions gases in specific member countries has given rise to sectorally more detailed single region CGE studies (e.g. Dellink et al., 2004). As expected, the general consensus is that meeting emissions reduction targets entails a short to medium term cost, but the differences in contexts and policies modelled render direct comparison of results difficult, or of little value. A cursory review of the relevant Spanish literature (Labandeira et al., 2004; 2009; Labandeira and Rodríguez, 2010; GonzálezEguino, 2011) suggests that GDP falls of between 0.1% and 1% by 2012 may result from emissions restrictions. 4 Spain has been permitted an emissions target of 15% above 1990 levels, rising to a projected 37% when heavy usage of Kyoto approved ‘flexibility mechanisms’ and carbon sinks are accounted for. 5 In the case of agricultural practice, a proportion of its pollution is classified as point source (i.e., emitted from a single discharge point such as a pipe). However, a large proportion is non-point source (difficult to determine an emitting source), which implies a more ‘diffuse’ nature to its emissions. 4 A key issue for this study is how the agriculture sector is impacted directly from facing its own emissions reduction targets, and indirectly from facing higher energy prices as a result of other environmental policies, such as the ETS. Given the diffuse nature of agricultural emissions, how reductions targets are to be achieved is left as an internal matter in each member state (European Parliament, 2009b) and is beyond the focus of the current study. Some CGE studies (Van Heerden et al. 2006; Labandeira and Rodríguez, 2006; Labandeira et al. 2009), report limited impacts on agriculture, but only account for emissions controls on combustion, whilst not accounting for agriculture’s diffuse emissions. One exception to this is a study assessing the Dutch economy by Dellink et al. (2004). The study estimates relatively sharper falls in agricultural production (- 4.8%) compared with the wider economy (-2.7%) by 2050, citing the relatively higher emissions intensity in agriculture (i.e., including non-CO2 gases). Given a general paucity of antecedents within the quantitative literature, there exists an additional need to assess the economic impacts of emissions targets on a selection of specific livestock and cropping practises. The focus on Spain is also justified by its strong growth record (pre-crisis) and the consequent sharp adjustment process it will need to follow in order to adhere to its emissions targets, 6 which is likely to have important implications on the agricultural sectors. In those Spanish case studies that exist, the CGE approach has been employed to examine the impact of meeting the Kyoto 2012 targets or other hypothetical short term policy targets (e.g. González-Eguino, 2011). A recursive dynamic CGE approach is employed in Bourne et al. (2012) which incorporates a contemporary baseline scenario to consider the emissions targets impacts of both Kyoto as well as the EU CEP in 2020. Employing a more agricultural focus, a further key feature of this study is the inclusion of all six GHGs emissions across Spanish sectors, whilst an explicit representation of EU agricultural policy mechanisms is coded to reflect the supply rigidities within EU agricultural factor and product markets and their concomitant impacts on agricultural emissions. The current paper follows the approach in Bourne et al. (2012) with two important extensions. On the one hand, agricultural production decisions are now subject to endogenous technological adaptation in response to tightening emissions controls. This characteristic is modelled via the calibration of non linear marginal abatement cost (MAC) functions 7 . Given that different agricultural activities have differing MAC functions, the ensuing abatement costs to agricultural 6 Although it is recognised that the economic slowdown precipitated by the financial crisis has had a positive effect on reduced Spanish GHG emissions. 7 For examples of agricultural MAC curves in the literature see Schneider et al. (2007); Beach at al. 2008; HöglandIsaksson et al. (2010). For a meta-analysis see Vermont and De Cara (2010). 5 sectors are expected to differ considerably from earlier estimates which implicitly assume equal adaptation rates. A further feature of this paper is the recognition of the feedback mechanism which exists between changes in global temperatures and land productivity. With some notable exceptions (e.g. Csicar et al, 2011; Steinbuks and Hertel, 2011), the majority of CGE studies do not consider the land productivity impacts which accrue under a 'no change' or status quo baseline scenario. Consequently, the economic costs of climate change mitigation strategies are biased downwards. The costs of climate change are famously complex and involve a great deal of both scientific and economic uncertainty. However, a first step towards incorporating such costs into CGE models such costs is made in this study through the inclusion of estimates of land productivity effects in the crops sectors. The rest of this paper is structured as follows: in section 2 the methodology and details of simulations are presented, with findings presented in section 3. Section 4 concludes and suggests some possible areas for further research. 2. Methodology This section presents a brief description of the model used, followed by an explanation of the major data sources, and a contribution from two collaborators (Sonia Quiroga and Zaira Fernandez-Haddad from the Universidad de Alcala) describing the econometric model they used to estimate the land productivity effects of climate change used in these simulations. 2.1 Model Framework The CGE framework is ‘demand’ driven, based on a system of neoclassical final, intermediate and primary demand functions. With the assumption of weak homothetic separability, a multi-stage optimisation procedure allows demand decisions to be broken into ‘nests’ to provide greater flexibility through the incorporation of differing elasticities of substitution. Moreover, accounting identities and market clearing equations ensure a general equilibrium solution for each year that the model is run. After appropriate elasticity values are chosen to allow model calibration to the database, and an appropriate split of endogenous and exogenous variables is selected (closure), specific exogenous macroeconomic or trade policy ‘shocks’ can be imposed to key variables (e.g., tax/subsidy rates, primary factor supplies, technical change variables, or real growth in GDP and/or its components). The model responds 6 with the interaction of economic agents within each market, where an outcome is characterised by a ‘counterfactual’ set of equilibrium conditions. To improve our estimates of the supply responsiveness of agricultural activities to emissions targets in the context of supply rigidities and support policies, additional code is implemented to support the representation of the Common Agricultural Policy (CAP). This follows previous CGE agricultural studies (see, for example, Philippidis and Hubbard, 2003) and is described in Table 2. As an important driver of (carbon dioxide) emissions, modifications are also made to the intermediate and final demands energy nests (Burniaux and Troung, 2002). Energy demands are separated from non-energy demands, where in the production nest they are treated as part of value added (rather than intermediate inputs) owing to the important relationship between (energy using) capital and energy. Furthermore, electrical and non-electrical (i.e., coal, gas, oil, bio-fuels) demands are in separate nests. For producers, this implies that primary energy (unlike electricity) can also be used as a ‘feedstock’ input into other industries (i.e., fertilizer, refining of raw energies) rather than directly consumed as an energy source. Changes in GHG emissions are assumed to be directly proportional to four driving mechanisms in the model (Rose and Lee, 2009): industrial processes (i.e., output), land use 8 and intermediate and final demands for fuels. 9 As a result, firms have some flexibility to mitigate their combustion emissions via substitution toward cleaner energy sources or less energy intensive capital, while process emissions can be reduced either by a contraction in industry output, or by end-of-pipe abatement determined by the MAC curve. Additional endogenous tax wedges, measured in Euros per metric tonne of CO2 equivalent, are inserted into the model code on each of the four drivers to capture the ‘shadow costs’ of reducing emissions (for sectors outside the ETS scheme), whilst the permit price for the ETS sectors (see below) is held exogenous and shocked according to data and projections (see below). For the MAC curves, ‘end-of-pipe’ abatement (see van Regemorter, 2005) response is calibrated to the data taken from GAINS through the use of a flexible functional form based on the work of De Cara and Jayet (2006). As a result, a more stringent emissions reduction target will (ceteris paribus) drive a higher ‘shadow cost’ to farmers in order for the target to be met, with the magnitude of this cost dependent on the steepness of the farmers’ MAC curves – i.e. the ease with which they can modify their production techniques in order to reduce emissions. In crop farming, for example, this could mean applying nitrogen fertiliser more strategically, rather than simply using less fertiliser, which would reduce 8 Methane released from rice-growing 9 For example, vegetable industry emissions from combustion of petrol are in direct proportion to intermediate input usage of petrol; production ‘process’ emissions vary with industry output. 7 land productivity. In the model, this end-of-pipe abatement then reduces the emissions factor associated with a specific emissions source, which in turn is also a function of the trend observed over the period 1990-2007. In other words, each emissions factor is made up of two components – an exogenous trend extrapolated from the available data (baseline and policy scenario), and endogenous, price-driven abatement (policy scenario only). Kyoto emissions reductions to 2012 are modelled by exogenous annual linear reductions in the number of domestic permits issued for the ETS sectors and the relevant emissions quota for non-ETS sectors. Spain is assumed to be a ‘price taker’ within the ETS (i.e., small country assumption), such that the permit price is held exogenous in all years. Following Labandeira and Rodriguez (2010), net imports of additional permits from other EU Member States by Spanish industries adjust endogenously subject to domestic demand conditions (determined by macroeconomic data and projections), gradual reductions in the exogenous supply of domestic permits, and year-on-year exogenous changes in the permit price. The purchase/sale of permits from/to other EU members is subsequently recorded as an additional import/export in the national accounts, adjusting the trade balance, and subsequently Spanish GDP. In keeping with the EU’s decision to initially allocate the majority of permits for free (employing a ‘historical’ emissions criterion), ETS permit allocation up to 2012 is via a ‘grandfathering’ method, whilst in the subsequent period (2013-2020), an increasing proportion of permits are auctioned at different rates (depending on the sector). Permit allocation is modelled by refunding the proportion of the cost incurred by firms in ‘buying’ grandfathered permits via a lump-sum subsidy payment, as set out in Edwards and Hutton (1999) and Parry (2002). Thus, in a given year, if 40% of a sector’s permits are auctioned, only 60% of the cost is refunded. Revenue raised from the auctioning of permits is paid, along with taxes on non-ETS sector emissions, to the government as tax revenue. 10 In the non-ETS sectors, the relevant abatement cost adjusts endogenously depending on the exogenous macro emissions targets. From 2013, a separate requirement for the diffuse sectors comes into force and their emissions quotas are adjusted accordingly. Given the lack of relevant Spanish data sources, calibration is facilitated through usage of substitution and expenditure elasticities from the standard GTAP version 8 data base (Aguiar et al., 2012). In the energy module, substitution elasticities from GTAP-E econometric estimates for developed countries are employed. Following Dixon and Rimmer (2002), export demand elasticities are calibrated to upper level GTAP Armington elasticities, whilst the transformation elasticities for land (between uses) are taken from Keeney and Hertel (2009). Central tendency 10 There are various hypothetical options for revenue recycling of environmental tax revenues (‘double dividend’) which lie beyond the scope of this study. 8 estimates of labour supply elasticities for Spain are taken from Fernándes-Val (2003) whilst for agro-food products, private household expenditure elasticities are taken from a study by Moro and Sckokai (2000) on Italian households stratified by wealth. 2.2 Data To support the construction of the accompanying CGE Spanish database, the input-output (IO) tables (year 2007) published by the Instituto Nacional de Estadística (INE) are a principle source of secondary data (INE, 2010). These data are supplemented by institutional accounts data from INE on direct taxes, social security contributions, savings, fiscal deficit etc. to make up a social accounting matrix (SAM) for Spain (see Pyatt and Round (1985) for a detailed explanation of SAMs). Importantly, the conditions imposed by the IO/SAM framework underlie the fundamental accounting conventions of the CGE model. For the purposes of this study, the aggregation focuses principally on agricultural activities, whilst remaining sectors are those identified within the EU ETS, the non-agricultural ‘diffuse sectors’ (see Table 1), and ‘residual’ manufacturing and services activities. The model has three broad factors (capital, labour and agricultural land), of which labour is further subdivided into ‘highly skilled’, ‘skilled’ and ‘unskilled’ employing labour force survey data (INE, 2009a). Additionally, to explore the distributive effects of policy changes, Household Survey Data (INE, 2009b) permit a disaggregation of private household purchases for up to eight distinct disposable income groupings. 11 UNFCCC (2011) Spanish submissions data on emissions are separated into fuel combustion; fugitive emissions; industrial processes; solvent and other product usage; land use, land use change and forestry (LULUCF); waste emissions; and agricultural emissions. The data set includes concordance by industry activity, although in some cases further disaggregation is required to map to the model sectors. For combustion emissions, UNFCCC data is combined with energy usage data from the International Energy Agency (IEA, 2011), and intermediate input data from the Spanish IO database (INE, 2010), to map emissions by (i) fuel type; (ii) industry and (iii) source (i.e., domestic/imported). Fugitive and industrial process emissions are assigned to specific IO industries following Rose and Lee (2009), whilst solvent and other product emissions all originate from the chemical industry. Waste emissions are apportioned between the IO sectors of market and non-market sanitation services, whilst LULUCF emissions are excluded from the 11 It should be noted that at the current time, no attempt is made to insert factor ownership shares by households. 9 current analysis. 12 Finally, Spanish agricultural emissions by activity are, in general, clearly disaggregated into specific agricultural activities within the UNFCCC database, although nitrogen run-off from agricultural soils is assigned employing additional data on land usage (MARM 2008) and nitrogen uptake for specific crops (MARM 2010). Finally, data for the MAC curves comes from the International Institute for Applied Systems Analysis’ (IIASA) ‘GAINS’ model 13 , which provides estimates of the cost and abatement potential for each currently available emissions reduction technology in the agricultural sector, for all Annex 1 countries, and some others. Abatement technologies available in Spain include feed changes and anaerobic digestion plants for livestock emissions, and the use of nitrification inhibitors and precision farming techniques for crops sector emissions from fertilisers. A detailed description of the technologies covered, and the methodology used, can be found in Höglund-Isaksson et al. (2010a). 2.3 Yield Changes Statistical models of yield response have proven useful to evaluate the sensitivity of land productivity to climate change (Parry et al., 2004; Ciscar et al., 2011; Iglesias et al., 2010). This methodology is applied to the eight most representative crops, in terms of area and value, in the Ebro basin. The Ebro river basin is located in the northeast of the Iberian Peninsula; with an area of 85000 km 2 and a mean annual runoff of 16.92 km 3 yr − 1 , and is the largest basin in Spain. The selected crops are alfalfa, wheat, rice, grapevine, olive, potato, maize and barley. To characterize crop yield for these Mediterranean crops, we estimate linear regression models by ordinary least squares (OLS) linking bio-physical and socio-economic factors, through the introduction of environmental, hydrological, technological, geographical and economic variables. Later, these models are used to assess climate change effects on crop yield. We use a CobbDouglas production function for eight main crops in the area using historical data (1984-2002). A Cobb-Douglas specification was chosen because of its intuitive interpretation in terms of elasticities, as well as its simplicity and validity (Zellner et al., 1966, Giannakas et al., 2003) and its acceptance in agricultural economics literature (Lobell et al., 2005, 2006; Quiroga et al., 2011). The specified model for the eight crops has the general form: 12 Whilst the UNFCCC data provide a figure for the total sequestration of land, due to data limitations, we were unable to disaggregate this sequestration potential between agricultural land types and forestry land. Moreover, due to the difficulty in valuing forestry land, the model does not have a land factor in the forestry sector. 13 http://www.iiasa.ac.at/web/home/research/researchPrograms/GAINS.en.html ++++++++= −− t6t5t4nt3t2t101tt area_Irrigebro_AreaAltitudeMacMacLYlnYln βββββββα 16 the land productivity extension should be seen as an attempt to improve the realism of the scenarios, rather than a core component of the model. The first priority for further work is to expand and improve the MAC curves. The current curves are based on a small number of data points, and yet they play a crucial role in the model results. Whilst this continues to be an avenue of further research, at the current time, to the best of the authors' knowledge, other secondary data estimates specific to the Spanish crops and livestock sectors are not available. A second area of great interest would be to improve the treatment of water as a resource in the model. 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