M ARTIN B ANSE , A NDREA R OTHE , A NDRZEJ T ABEAU , H ANS VAN M EIJL AND G EERT
3. Scenario results 1 Scenario description
3.3 Scenario results
As already mentioned, the main goal is to illustrate the impact of changing factor mobility. However, the next two figures show the impact of a worldwide implementation of biofuel policies on world agricultural prices and land use to give a first glance at the underlying ‘scenery’.
Figure 10.1 Change in real world prices in 2020 relative to no binding biofuel mandates, %
Source: Own calculations.
World prices of agricultural products tend to increase with enhanced biofuel consumption as a consequence of biofuel policies. This is especially the case for those products that are directly used as biofuel crops. Figure 10.1 presents the changes in real agricultural prices relative to a situation without (binding) biofuel policies. Under biofuel mandates international grain and oilseed prices increase by more than 25% relative to the ‘no biofuel’ scenario.
In all regions, mandatory blending also leads to a moderate increase in total primary agricultural output and consequently higher land demand (Figure 10.2). Land use increases in all regions compared with no binding biofuel mandates. With mandatory biofuel policies implemented on a global scale, agricultural land use increases by around 4.5%.
0 5 10 15 20 25 30
Crops Biofuel Crops Grains Oilseeds
106|BANSE,ROTHE,TABEAU, VAN MEIJL &WOLTJER
Figure 10.2 Change in agricultural land use in 2020 relative to no binding biofuel mandates, %
Notes: All BioF Reg covers all regions listed above with the two subsets ‘EU&US’ and ‘Rest-Mandat’. All other regions that do not apply binding biofuel policies are aggregated under ‘NoBioF-Reg.’
Source: Own calculations.
These results should illustrate the general tendencies after biofuel mandates have been implemented on a global scale in different countries and regions. The following graphs show how the significant impact on agricultural markets in term of price changes, production and land uses might alter if capital becomes more mobile at the intra-sectoral level (i.e.
within the agricultural sector with a higher substitutability between capital and other factors) and at the inter-sectoral level (i.e. between the agricultural and the non-agricultural sectors) with a higher (factor-) price responsiveness to changes in the ratio capital use within the agricultural and the non-agricultural part of the economy.
Similar to the presentation of the general outcome of the implementation of biofuel polices, we show the impact on world agricultural prices.
0 2 4 6 8 10 12 14
World All BioF Reg. EU&US Rest-Mandat NoBioF-Reg.
ACCESS TO CAPITAL AND AGRICULTURAL LAND DEMAND |107 Figure 10.3 CES elasticities: change in real world agricultural prices in 2020
relative to standard CES elasticity values under Glob-BFM scenario, %
Source: Own calculations.
A variation of intra-sectoral mobility of capital due to change in the CES elasticity of capital in the production function has only limited impact on world agricultural prices. With lower CES elasticities, which imply a stickier and ‘slower’ change in the composition of factor use under changing factor prices, world prices of crops used for biofuel production are slightly higher. With higher CES elasticities, wheat prices will be around 0.5% lower compared with the standard elasticity setting in the Glob-BFM scenario (Figure 10.3).
Figure 10.4 CET elasticities: change in real world agricultural prices in 2020 relative to standard CET elasticity values under Glob-BFM scenario, %
Source: Own calculations.
-0.60 -0.40 -0.20 0.00 0.20 0.40 0.60
Crops Biofuel-Crops Wheat Grains Oilseeds CES-Cap-min75 CES-Cap-min50 CES-Cap-plus50 CES-Cap-plus100
-25.0 -15.0 -5.0 5.0 15.0 25.0
Crops Biofuel-Crops Wheat Grains Oilseeds Mobile-min75 Mobile-min50 Mobile-plus50 Mobile-plus100
108|BANSE,ROTHE,TABEAU, VAN MEIJL &WOLTJER
If we assume an increase in inter-sectoral mobility of factors between agricultural and non-agricultural sectors, the impact of world agricultural prices become more evident. With lower inter-sectoral factor mobility, we see that for all arable crops used for biofuel production world prices are much higher compared with the standard CET elasticity values under the Glob-BFM scenario (Figure 10.4). Under CET elasticities, which are 75%
lower compared to the standard assumptions, world prices for wheat are more than 20% higher. Higher inter-sectoral factor mobility will dampen the increase in world prices and with CET elasticities twice as high compared to the standard setting, wheat prices will be more than 5% lower compared to the standard assumptions under the Glob-BFM scenario.
How do these results correspond to the changes in agricultural production? Under lower inter-sectoral factor mobility we observe a higher level of agricultural prices than under the standard assumption. Figure 10.5 shows the impact of a systematical variation in the CET elasticities on the level of agricultural production. The higher level of prices under lower inter-sectoral mobility is mirrored by a higher level of agricultural production, which is at first sight a little bit counter-intuitive. Lower inter-sectoral mobility means lower use of labour and capital compared to the standard scenario outcome. This is, however, only part of the picture! In agriculture, land is sector-specific and acts as the limiting factor to agricultural production. With higher prices, land rents also increase and it becomes profitable to expand land use (see Figure 10.6). Under lower factor mobility, agricultural production becomes more land-intensive and less labour/capital-intensive. Hence land use increases dramatically on the global scale.
The asymmetric figure of price change (i.e. higher increases in prices/production under low factor mobility and relatively lower decreases in prices/production under higher factor mobility) is due to the sector-specificity of land in agriculture where, for most arable crop products, land rents are the largest part of total value added and the mobile part of labour and capital gains make up only a relatively small share of total value added in arable crop production.
ACCESS TO CAPITAL AND AGRICULTURAL LAND DEMAND |109 Figure 10.5 CET elasticities: change in world agricultural production in 2020
relative to standard CET elasticity values under Glob-BFM scenario, %
Source: Own calculations.
With lower inter-sectoral factor mobility, agricultural land use expands as a consequence of biofuel mandates implemented on a global scale by almost 290 million hectares, which is equivalent to 5.4% of global agricultural land use. Higher inter-sectoral factor mobility will ease the pressure on expanding agricultural land use and around 80 million hectares less will used compared with the standard assumption of factor mobility. Here, employment and capital use in agricultural increases. If the inter-sectoral factor mobility is altered for capital only, the effects become much smaller (right-hand side of Figure 10.6).
-10.0 -5.0 0.0 5.0 10.0
Crops Biofuel-Crops Wheat Grains Oilseeds Mobile-min75 Mobile-min50 Mobile-plus50 Mobile-plus100
110|BANSE,ROTHE,TABEAU, VAN MEIJL &WOLTJER
Figure 10.6 CET elasticities: change in agricultural land use in 2020 compared with standard CET elasticity values under Glob-BFM scenario, million ha
Source: Own calculations.
4. Conclusions
This chapter shows the consequences of different degrees of factor mobility in agricultural production under the assumption of enhanced biofuel production in those regions and countries of the world which have implemented biofuel policies in the form of mandatory blending targets of transportation fuels. The chosen quantitative modelling approach is the multi-sectoral economic MAGNET model with a systematical variation of the inter-sectoral and intra-sectoral factor mobility.
The simulation results of the model show that biofuel policies have a pronounced impact on the markets for grains, oilseeds and sugar, but a rather limited impact on the production level of aggregated primary agricultural output. At the global level, the EU and US biofuel policies contribute to the increasing demand for biofuel crops. However, other countries that also introduced mandatory biofuel targets, such as Brazil, Canada, India, Philippines, South Africa and Thailand, contribute to an even greater extent to increasing world prices for agricultural products driven by food use for fuel.
With increasing agricultural output, total agricultural area is projected to increase by 5%, while production of biofuel crops increases by around 19% indicating a more intensive production of biofuel crops at the
-300 -200 -100 0 100 200 300
Mobile-min75 Mobile-min50 Mobile-plus50
Mobile-plus100 Mobile-Cap-min75 Mobile-Cap-min50 Mobile-Cap-plus50 Mobile-Cap-plus100
all factors
ACCESS TO CAPITAL AND AGRICULTURAL LAND DEMAND |111 global level. Even the strong increase in crop production in countries implementing biofuel policies exceeds domestic supply, and the imports of these biofuel crops from other parts of the world which do not implement biofuel policies are projected to increase significantly.
The analysis shows that apart from direct effects of an enhanced demand for bioenergy on production and land use, the indirect effects of biofuel policies dominate. Additional production of biofuel crops within and outside countries with voluntary and mandatory biofuel policies leads to strong indirect land-use changes and associated GHG emissions.
The systematical variation of factor mobility indicates that the
‘burden’ of global biofuel policies is not equally distributed across different factors within agricultural production. Agricultural land as the pre-dominant and sector-specific factor is, regardless of the degree of inter-sectoral or intra-inter-sectoral factor mobility, the most important factor and limits the expansion of agricultural production. More capital and higher employment in agriculture eases the pressure on additional land use, but only partly. To expand agricultural production on the global scale would require adapting both land and mobile factors to increase total factor productivity in agriculture in the most efficient way.
References
Al-Riffai, P., B. Dimaranan and D. Laborde (2010), Global Trade and Environmental Impact Study of the EU Biofuels Mandate, ATLASS Consortium Final Report.
Banse, M., H. van Meijl, A. Tabeau and G. Woltjer (2008),” Will EU Biofuel Policies Affect Global Agricultural Markets?”, European Review of Agricultural Economics, 35(2):117–141.
Dehue, B. and W. Hettinga (2008), Land Use Requirements of Different EU Biofuel Scenarios in 2020, Utrecht: Ecofys.
Dimaranan, B.V. (ed.) (2006), Global Trade, Assistance, and Production: The GTAP 6 Data Base, Center for Global Trade Analysis, Purdue University, Indiana.
Eickhout B., G.J. van den Born, J. Notenboom, M. van Oorschot, J.P.M. Ros, D.P.
van Vuuren and H.J. Westhoek (2008), Local and Global Consequences of the EU Renewable Directive for Biofuels: Testing the Sustainability Criteria, MNP Report 500143001/2008, Netherlands Environmental Assessment Agency.
Hertel, T.W. (ed.) (1997), Global Trade Analysis: Modeling and Applications, Cambridge: Cambridge University Press.
Hertel, T.W., W.E. Tyner and D.K. Birur (2010), “The Global Impacts of Biofuel Mandates”, The Energy Journal, 31(1):75-100.
112|BANSE,ROTHE,TABEAU, VAN MEIJL &WOLTJER
Keeney, R. and T.W. Hertel (2005), “GTAP-AGR: A Framework for Assessing the Implications of Multilateral Changes in Agricultural Policies”, GTAP Technical Paper No. 1869, Center for Global Trade Analysis, Purdue University.
Mulligan, D., R. Edwards, L. Marelli, N. Scarlat, M. Brandao and F. Monforti-Ferrario (2010), “The Effects of Increased Demand for Biofuel Feedstocks on the World Agricultural Markets and Areas”, JRC Scientific and Technical Reports, Outcomes of a Workshop, European Commission Joint Research Centre, Institute for Energy, Ispra, Italy, 10–11 February.
Nowicki, P., H. van Meijl, A. Knierim, M. Banse, J. Helming, O. Margraf, B.
Matzdorf, R. Mnatsakanian, M. Reutter, I. Terluin, K. Overmars, C. Verhoog, C. Weeger and H. Westhoek (2009), Scenar 2020 – Scenario study on agriculture and the rural world, European Commission, Directorate-General Agriculture and Rural Development, Brussels.
OECD (2008), Economic Assessment of Biofuel Support Policies, Directorate for Trade and Agriculture, Paris.
Rajagopal, D. and D. Zilberman (2007), “Review of Environmental, Economic and Policy Aspects of Biofuels”, Policy Research Working Paper No. 4341, World Bank, Washington, D.C.
Sorda, G., M. Banse and C. Kemfert (2010), “An Overview of Biofuel Policies Across the World”, Energy Policy, 38(11):6977–6988.
Shutes, L. A. Rothe and M. Banse (2012), “Factor Markets in Applied Equilibrium Models: The Current State and Planned Extensions Towards an Improved Presentation of Factor Markets in Agriculture”, Factor Markets Working Paper No. 23, Centre for European Policy Studies, Brussels.
van Meijl, H., T. van Rheenen, A. Tabeau and B. Eickhout (2006), “The Impact of Different Policy Environments on Agricultural Land Use in Europe”, Agriculture, Ecosystems and Environment, 114:21–38
Woltjer, G. and M. Kuiper (lead authors) (2013), “The MAGNET model. Module description”, LEI-WUR, The Hague.
| 113