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The potential for bioenergy crops to contribute to meeting GB heat and electricity demands

The potential for bioenergy crops to contribute to meeting GB heat and electricity demands Introduction Rapidly growing energy demand and global warming are the two motivations for the potential deployment of bioenergy. Bioenergy has potential energy, environmental and economic advantages over many current energy sources (Schmer et al ., ), and is recognized as having a potentially important contribution to the UK Government's energy and environment objectives, including energy security and the reduction in greenhouse gas (GHG) emissions (DTI (The UK Department of Trade & Industry), ; Taylor, ). The potential for using bioenergy crops to reduce GHG emissions and to supply heat and electricity has been explored in many recent studies (Smith et al ., , b ; Bauen et al ., ; Wang et al ., , b ), and has been found to have the greatest carbon mitigation potential of all land‐based mitigation options examined (Smith et al ., , b ). In addition, the availability of more efficient technologies, such as combined heat and power (CHP) plants, provides additional options for bioenergy crops (Wang et al ., ). Among bioenergy crops, Miscanthus × giganteus ( Miscanthus ) and short rotation coppice (SRC; in GB often willow and poplar species e.g. Salix. viminalis L. x S. viminalis var Joruun ) have received much attention, because these are believed to be the best yielding fast‐growing species in the GB (Aylott et al ., ; Bauen et al ., ), have a higher energy ratio of output to input and can be effective in the mitigation of GHGs (St. Clair et al ., ). The United Kingdom has an ambitious renewable energy target of a 15% share of renewable energy in total energy by 2020. This target requires 32% renewable electricity and 14% renewable heat (BERR (The UK Department for Business, Enterprise & Regulator Reform), ). Therefore, increases in electricity from 19 TWh in 2006 to 120 TWh by 2020 and in heat from 4 TWh in 2006 to 90 TWh by 2020 are required (Bauen et al ., ). Meeting these demands will be a challenge; in 2008, biomass accounted for only 2.3% of electricity generation and satisfied less than 1% of heat demand (Bauen et al ., ). Therefore, a high resolution estimation of the potential contribution of Miscanthus and SRC to heat and electricity in GB is necessary. In this study, for the first time, we provide high‐resolution estimates of the potential contribution of Miscanthus and SRC to meeting heat and electricity demands in GB, using a model system, which includes a partial equilibrium model and process‐based terrestrial biogeochemistry models. Materials and methods Miscanthus is a C4 species, which can more effectively use sunlight and water than C3 species (Knapp, ; Wang et al ., ). Its yields peak towards the end of autumn, at approximately 13 tonnes per hectare (t dry matter ha −1 ) in the United Kingdom (DEFRA (The UK Department for the Environment, Food & Rural Affairs), ), and about 67% of this peak yield is harvested in the following spring when the crop has senesced, repartitioned nutrients to the rhizomes and dried. SRC is recognized by the UK Committee on Climate Change as the most suitable energy crop for current GB conditions, and can be grown productively on both former arable and pasture land, with the average yield being roughly 15 t dry matter ha −1 yr −1 (Andersen et al ., ; Aylott et al ., , b ). The contribution of Miscanthus and SRC to heat and electricity demands was estimated based on a model system, which consists of a partial equilibrium model based on the supply chain of bioenergy crops with life‐cycle analysis described previously in Wang et al . ( ) and the process‐based terrestrial biogeochemistry models: MiscanFor (Hastings et al ., ; Wang et al ., ) and ForestGrowth‐SRC Model (Tallis et al ., ). In this system, MiscanFor and ForestGrowth‐SRC models were used to generate the yields for Miscanthus and SRC, respectively, and the partial equilibrium model was used to define how the optimal suitability of two bioenergy to supply heat and nonheating electricity (hereafter referred to simply as electricity) demands described in Taylor et al . ( ). The partial equilibrium model is a demand‐driven optimization energy model, which maximizes the profit of the whole energy system while ensuring that the energy demand is met (Wang et al ., ). The model does not only determine the optimal locations but also the capacity sizes for energy facilities. The main input parameters included maximum theoretical energy potential, energy cost, the efficiency and cost of the energy technologies, transportation cost and energy demand. The projected yields by MiscanFor and ForestGrowth‐SRC have shown good agreement with the field observations. The main input parameters for these two models were the solar irradiation, precipitation and temperature, and soil properties such as soil temperature (Hastings et al ., ; Tallis et al ., ). The distribution of yields for Miscanthus and SRC was generated using UKCP09 meteorological forcing data (for 2010) (Murphy et al ., ) and soil data from the harmonized world soil database (FAO/IIASA/ISRIC/ISSCAS/JRC, ). Miscanthus is harvested annually and the peak yield estimated by the model was scaled by 0.67 to obtain the available yield due to drying the following spring (Hastings et al ., ). The rotation of SRC is typically set to 3 years (Armstrong, ; Aylott et al ., ). The demand data for heat and nonheating electricity were obtained at 1 km resolution by disaggregating local gas and electricity consumption data (DECC (The UK Department of Energy & Climate Change), ) as described by Taylor et al . ( ). The resulting heat demand data include energy used for space and water heating, supplied by both fossil fuels and electricity. Cooking and industrial uses of heat were considered ineligible for supply by bioenergy and therefore excluded. The nonheating electricity data consist of electricity used for all nonheating purposes, comprising mainly lighting and appliances in the domestic sector and a wider range including motors, cooling, and ventilation in the nondomestic sector. Domestic and nondomestic heat demand data sets were derived separately because different methods were required, and then combined to give the total (domestic plus nondomestic) heat demand for each grid cell, with a similar procedure for nonheating electricity demand (Taylor et al ., ). To examine the maximal capacity of bioenergy use to meet the heat and electricity demands, we assume that demand is first met from bioenergy crops, wherever the bioenergy crops are available, and that other energy sources are then considered, where the availability of bioenergy crops does not satisfy demand. As described in Lovett et al. ( ), a constraint map with a high resolution (100 m) was used to mask areas that are unsuitable for bioenergy crops, and thereby determine the potential land areas for bioenergy crops. The constraint map is derived from a Land Suitability Classification system, which is based on a Sustainability Appraisal Framework, and considers absolute prohibitions (e.g. protected habitats and scheduled historical monuments) and relative limits where planting up to certain thresholds would not impinge upon sustainability objectives. The criteria used to determine land suitability can be found in Table (from Lovett et al ., ). Criteria used to determine land suitability Item Criteria Roads, Rivers & Urban areas No Slope ≤15% Monuments & Heritage sites No Designated areas No Woodland No Peat soils Soil organic carbon > 30% Natural habitats No Improved grassland No National Parks and AONBs No Bioenergy is assumed to be the feedstock for CHP, which can generate heat and electricity simultaneously, and reduce carbon emissions by up to 30% compared to separate means of conventional generation (e.g. a boiler and power station) (DECC (The UK Department of Energy & Climate Change), ). The systematic efficiency of CHP is assumed to be 85% and the ratio of heat to electricity is 2 : 1 (DECC (The UK Department of Energy & Climate Change), ). The potential CHP position is derived from DECC (The UK Department of Energy & Climate Change) ( ). The cost data for CHP were taken from Wang et al . ( ). To allow optimization, all data are then standardized to a 10 km national grid, and only heat and power are considered in this analysis. Alternative use of the land and the farm‐scale economics of growing bioenergy crops compared to alternative uses for the land were not considered here, and are described in Alexander et al . ( ). In this study, we derive the potential distribution of energy crops from an energy economics perspective. When aggregating the constraint map at 100 m resolution to 10 km, cells with a potential area of less than 3 ha within the larger 10 km cell were excluded as small areas such as these are not viable for energy crop production. The single scenario modelled considered the yield of bioenergy crops grown under present (2010) meteorological and soil conditions with current crop varieties and agronomic practices, current land‐use distribution, and the present (2010) heat and energy demand of the United Kingdom. Results Strong contrasts in yield are found between Miscanthus and SRC across GB. Miscanthus grows best in Wales, and the Northwest and Southwest of England, whereas the highest yield of SRC occurs in the South of Scotland, Wales and the Northwest of England (Fig. ). This is partly due to the different biophysical characteristics of Miscanthus and SRC. Tuck et al . ( ) derived simple rules of climate and elevation for Miscanthus and SRC. SRC has a larger range of elevation and rainfall, and withstands colder temperatures than Miscanthus . The distribution maps of heat and electricity demand are rather similar (Taylor et al ., ). As expected, the highest demand for heat and electricity is seen for large cities (Fig. ), which have a high population density (Taylor et al ., ). Yields of energy crops. Left is the yield of Miscanthus and right is the yield of SRC . Heat and electricity demand. Left is the heat demand and right is the electricity demand. The optimal areas for Miscanthus growth occur in the Midlands and the parts of the South of GB, whereas the optimal areas for SRC growth occur mostly in Scotland, the Midlands and parts of the South of GB (Fig. ). The optimal area for Miscanthus is larger than for SRC (Table ), due to Miscanthus having a better marginal value than SRC (ADAS, ), and the model being cost‐based driven (Wang et al ., ). The mixed contribution of Miscanthus and SRC to heat and electricity is significant (Table ). With 8 Mha of land area for Miscanthus and SRC, the contribution reaches 224 800 GWh yr −1 to heat and 112 500 GWh yr −1 to electricity, accounting for 66% of total heat demand and 62% of total electricity demand, respectively, although this analysis does not include the farm level economic viability of bioenergy supply (see Alexander et al ., ). Optimal area grown for Miscanthus and SRC Miscanthus area grown (Mha) SRC area grown (Mha) Total area grown (Mha) 4.7 3.3 8.0 Contribution to electricity and heat demand Heat (GWh yr −1 ) Electricity (GWh yr −1 ) 224 800 (66%) 112 500 (62%) Optimal area grown for bioenergy crops. Discussion To meet the United Kingdom's 15% renewable energy target by 2020, it is estimated that biomass needs to contribute 8% of renewable electricity and 60% of renewable heat (BERR (The UK Department for Business, Enterprise & Regulator Reform), ). Without considering farm‐scale economic constraints or other land use, Miscanthus and SRC could generate economically competitive 224 800 GWh yr −1 heat and 112 500 GWh yr −1 electricity, accounting for 66% of total heat demand and 62% of total electricity demand respectively. Andersen et al . ( ) estimated that 5% uptake of SRC in the Scotland would provide an electricity production potential of about 163 MWh yr −1 . The optimal spatial distribution for growing Miscanthus and SRC is obviously largely driven by the distribution of heat and electricity demand, the distribution of yields of Miscanthus and SRC, and the potential locations of CHP plants. The potential spatial distribution of Miscanthus and SRC energy crop areas to meet the demand is an important step in determining the ultimate distribution of these crops, when farm‐scale economics are taken into account. If the potential distribution of crops or attainable yield changes in response to environmental change, the optimal area might also change, as may the suitability of each crop compared to the other. The dynamically determined distribution of crop areas also provides a dynamic framework to estimate GHG emissions from land‐use change, as advocated by Melillo et al . ( ), rather than the use of static frameworks employed elsewhere. Energy crops are mostly grown in high yield areas, in which the production cost is relatively low. The high‐yield areas chosen reduce the deployment of energy crops on existing land with a natural ecosystem, and thus decrease potential GHG emissions from direct land‐use change (St. Clair et al ., ; Hillier et al ., ), although this will result in competition with food crops (Valentine et al ., ) and could lead to indirect land‐use change elsewhere (Fargione et al ., ). Using biomass for energy is likely to have both positive and negative competitive effects on food production and therefore on land use, although the reasons for growing crops for bioenergy are complex (Smith et al ., ). Alternatively, energy crops could be grown on land less suitable or unsuitable for food crops (Valentine et al ., ). The economics of growing crops for energy or for food are considered in Alexander et al . ( ). The distribution of energy crops has implications for low‐carbon sustainable development in the United Kingdom, which may require the systematic deployment of energy crops, rather than sparse deployment; in the United States, Heaton et al . ( ) reported that using Miscanthus to meet US biofuel goal will require less land if managed systematically than if allowed to develop unmanaged. The deployment of energy crops will result in the emission of some life‐cycle GHGs. Life‐cycle GHG emissions have a strong relationship with initial soil carbon stocks (Hillier et al ., ) and the land use they replace, as well as the agricultural management during their growth (Wang et al ., ). The cost of the life‐cycle GHG emissions will impact the final optimal deployment of energy crops (Wang et al ., ). The integration of this cost would require additional considerations: the possible ways of combining the cost include (i) setting a carbon cap for the energy crops, and/or (ii) translating the GHG cost to an economic cost, and then using the GHG emissions as a term in the partial equilibrium model. In addition to the impact on life‐cycle GHGs, the deployment of energy crops will also impact other ecosystem services. Miscanthus had higher ground flora diversity during the first 3 years after establishment compared with conventional cash crops (Semere & Slater, ), and had positive impacts on spider, beetle and earthworm diversity (Christian et al ., ). The environmental impacts of deployment of energy crops are further considered in Milner et al . ( ). Acknowledgements The study was supported by the UK Energy Research Centre (UKERC) via a UKERC/Natural Environment Research Council award: ‘Spatial Mapping and Evaluation of Energy Crop Distribution in Great Britain to 2050′ (NE/H013415/1). PS is a Royal Society‐Wolfson Research Merit Award holder. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png GCB Bioenergy Wiley

The potential for bioenergy crops to contribute to meeting GB heat and electricity demands

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Wiley
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"Copyright © 2014 John Wiley & Sons Ltd"
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1757-1693
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1757-1707
DOI
10.1111/gcbb.12123
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Abstract

Introduction Rapidly growing energy demand and global warming are the two motivations for the potential deployment of bioenergy. Bioenergy has potential energy, environmental and economic advantages over many current energy sources (Schmer et al ., ), and is recognized as having a potentially important contribution to the UK Government's energy and environment objectives, including energy security and the reduction in greenhouse gas (GHG) emissions (DTI (The UK Department of Trade & Industry), ; Taylor, ). The potential for using bioenergy crops to reduce GHG emissions and to supply heat and electricity has been explored in many recent studies (Smith et al ., , b ; Bauen et al ., ; Wang et al ., , b ), and has been found to have the greatest carbon mitigation potential of all land‐based mitigation options examined (Smith et al ., , b ). In addition, the availability of more efficient technologies, such as combined heat and power (CHP) plants, provides additional options for bioenergy crops (Wang et al ., ). Among bioenergy crops, Miscanthus × giganteus ( Miscanthus ) and short rotation coppice (SRC; in GB often willow and poplar species e.g. Salix. viminalis L. x S. viminalis var Joruun ) have received much attention, because these are believed to be the best yielding fast‐growing species in the GB (Aylott et al ., ; Bauen et al ., ), have a higher energy ratio of output to input and can be effective in the mitigation of GHGs (St. Clair et al ., ). The United Kingdom has an ambitious renewable energy target of a 15% share of renewable energy in total energy by 2020. This target requires 32% renewable electricity and 14% renewable heat (BERR (The UK Department for Business, Enterprise & Regulator Reform), ). Therefore, increases in electricity from 19 TWh in 2006 to 120 TWh by 2020 and in heat from 4 TWh in 2006 to 90 TWh by 2020 are required (Bauen et al ., ). Meeting these demands will be a challenge; in 2008, biomass accounted for only 2.3% of electricity generation and satisfied less than 1% of heat demand (Bauen et al ., ). Therefore, a high resolution estimation of the potential contribution of Miscanthus and SRC to heat and electricity in GB is necessary. In this study, for the first time, we provide high‐resolution estimates of the potential contribution of Miscanthus and SRC to meeting heat and electricity demands in GB, using a model system, which includes a partial equilibrium model and process‐based terrestrial biogeochemistry models. Materials and methods Miscanthus is a C4 species, which can more effectively use sunlight and water than C3 species (Knapp, ; Wang et al ., ). Its yields peak towards the end of autumn, at approximately 13 tonnes per hectare (t dry matter ha −1 ) in the United Kingdom (DEFRA (The UK Department for the Environment, Food & Rural Affairs), ), and about 67% of this peak yield is harvested in the following spring when the crop has senesced, repartitioned nutrients to the rhizomes and dried. SRC is recognized by the UK Committee on Climate Change as the most suitable energy crop for current GB conditions, and can be grown productively on both former arable and pasture land, with the average yield being roughly 15 t dry matter ha −1 yr −1 (Andersen et al ., ; Aylott et al ., , b ). The contribution of Miscanthus and SRC to heat and electricity demands was estimated based on a model system, which consists of a partial equilibrium model based on the supply chain of bioenergy crops with life‐cycle analysis described previously in Wang et al . ( ) and the process‐based terrestrial biogeochemistry models: MiscanFor (Hastings et al ., ; Wang et al ., ) and ForestGrowth‐SRC Model (Tallis et al ., ). In this system, MiscanFor and ForestGrowth‐SRC models were used to generate the yields for Miscanthus and SRC, respectively, and the partial equilibrium model was used to define how the optimal suitability of two bioenergy to supply heat and nonheating electricity (hereafter referred to simply as electricity) demands described in Taylor et al . ( ). The partial equilibrium model is a demand‐driven optimization energy model, which maximizes the profit of the whole energy system while ensuring that the energy demand is met (Wang et al ., ). The model does not only determine the optimal locations but also the capacity sizes for energy facilities. The main input parameters included maximum theoretical energy potential, energy cost, the efficiency and cost of the energy technologies, transportation cost and energy demand. The projected yields by MiscanFor and ForestGrowth‐SRC have shown good agreement with the field observations. The main input parameters for these two models were the solar irradiation, precipitation and temperature, and soil properties such as soil temperature (Hastings et al ., ; Tallis et al ., ). The distribution of yields for Miscanthus and SRC was generated using UKCP09 meteorological forcing data (for 2010) (Murphy et al ., ) and soil data from the harmonized world soil database (FAO/IIASA/ISRIC/ISSCAS/JRC, ). Miscanthus is harvested annually and the peak yield estimated by the model was scaled by 0.67 to obtain the available yield due to drying the following spring (Hastings et al ., ). The rotation of SRC is typically set to 3 years (Armstrong, ; Aylott et al ., ). The demand data for heat and nonheating electricity were obtained at 1 km resolution by disaggregating local gas and electricity consumption data (DECC (The UK Department of Energy & Climate Change), ) as described by Taylor et al . ( ). The resulting heat demand data include energy used for space and water heating, supplied by both fossil fuels and electricity. Cooking and industrial uses of heat were considered ineligible for supply by bioenergy and therefore excluded. The nonheating electricity data consist of electricity used for all nonheating purposes, comprising mainly lighting and appliances in the domestic sector and a wider range including motors, cooling, and ventilation in the nondomestic sector. Domestic and nondomestic heat demand data sets were derived separately because different methods were required, and then combined to give the total (domestic plus nondomestic) heat demand for each grid cell, with a similar procedure for nonheating electricity demand (Taylor et al ., ). To examine the maximal capacity of bioenergy use to meet the heat and electricity demands, we assume that demand is first met from bioenergy crops, wherever the bioenergy crops are available, and that other energy sources are then considered, where the availability of bioenergy crops does not satisfy demand. As described in Lovett et al. ( ), a constraint map with a high resolution (100 m) was used to mask areas that are unsuitable for bioenergy crops, and thereby determine the potential land areas for bioenergy crops. The constraint map is derived from a Land Suitability Classification system, which is based on a Sustainability Appraisal Framework, and considers absolute prohibitions (e.g. protected habitats and scheduled historical monuments) and relative limits where planting up to certain thresholds would not impinge upon sustainability objectives. The criteria used to determine land suitability can be found in Table (from Lovett et al ., ). Criteria used to determine land suitability Item Criteria Roads, Rivers & Urban areas No Slope ≤15% Monuments & Heritage sites No Designated areas No Woodland No Peat soils Soil organic carbon > 30% Natural habitats No Improved grassland No National Parks and AONBs No Bioenergy is assumed to be the feedstock for CHP, which can generate heat and electricity simultaneously, and reduce carbon emissions by up to 30% compared to separate means of conventional generation (e.g. a boiler and power station) (DECC (The UK Department of Energy & Climate Change), ). The systematic efficiency of CHP is assumed to be 85% and the ratio of heat to electricity is 2 : 1 (DECC (The UK Department of Energy & Climate Change), ). The potential CHP position is derived from DECC (The UK Department of Energy & Climate Change) ( ). The cost data for CHP were taken from Wang et al . ( ). To allow optimization, all data are then standardized to a 10 km national grid, and only heat and power are considered in this analysis. Alternative use of the land and the farm‐scale economics of growing bioenergy crops compared to alternative uses for the land were not considered here, and are described in Alexander et al . ( ). In this study, we derive the potential distribution of energy crops from an energy economics perspective. When aggregating the constraint map at 100 m resolution to 10 km, cells with a potential area of less than 3 ha within the larger 10 km cell were excluded as small areas such as these are not viable for energy crop production. The single scenario modelled considered the yield of bioenergy crops grown under present (2010) meteorological and soil conditions with current crop varieties and agronomic practices, current land‐use distribution, and the present (2010) heat and energy demand of the United Kingdom. Results Strong contrasts in yield are found between Miscanthus and SRC across GB. Miscanthus grows best in Wales, and the Northwest and Southwest of England, whereas the highest yield of SRC occurs in the South of Scotland, Wales and the Northwest of England (Fig. ). This is partly due to the different biophysical characteristics of Miscanthus and SRC. Tuck et al . ( ) derived simple rules of climate and elevation for Miscanthus and SRC. SRC has a larger range of elevation and rainfall, and withstands colder temperatures than Miscanthus . The distribution maps of heat and electricity demand are rather similar (Taylor et al ., ). As expected, the highest demand for heat and electricity is seen for large cities (Fig. ), which have a high population density (Taylor et al ., ). Yields of energy crops. Left is the yield of Miscanthus and right is the yield of SRC . Heat and electricity demand. Left is the heat demand and right is the electricity demand. The optimal areas for Miscanthus growth occur in the Midlands and the parts of the South of GB, whereas the optimal areas for SRC growth occur mostly in Scotland, the Midlands and parts of the South of GB (Fig. ). The optimal area for Miscanthus is larger than for SRC (Table ), due to Miscanthus having a better marginal value than SRC (ADAS, ), and the model being cost‐based driven (Wang et al ., ). The mixed contribution of Miscanthus and SRC to heat and electricity is significant (Table ). With 8 Mha of land area for Miscanthus and SRC, the contribution reaches 224 800 GWh yr −1 to heat and 112 500 GWh yr −1 to electricity, accounting for 66% of total heat demand and 62% of total electricity demand, respectively, although this analysis does not include the farm level economic viability of bioenergy supply (see Alexander et al ., ). Optimal area grown for Miscanthus and SRC Miscanthus area grown (Mha) SRC area grown (Mha) Total area grown (Mha) 4.7 3.3 8.0 Contribution to electricity and heat demand Heat (GWh yr −1 ) Electricity (GWh yr −1 ) 224 800 (66%) 112 500 (62%) Optimal area grown for bioenergy crops. Discussion To meet the United Kingdom's 15% renewable energy target by 2020, it is estimated that biomass needs to contribute 8% of renewable electricity and 60% of renewable heat (BERR (The UK Department for Business, Enterprise & Regulator Reform), ). Without considering farm‐scale economic constraints or other land use, Miscanthus and SRC could generate economically competitive 224 800 GWh yr −1 heat and 112 500 GWh yr −1 electricity, accounting for 66% of total heat demand and 62% of total electricity demand respectively. Andersen et al . ( ) estimated that 5% uptake of SRC in the Scotland would provide an electricity production potential of about 163 MWh yr −1 . The optimal spatial distribution for growing Miscanthus and SRC is obviously largely driven by the distribution of heat and electricity demand, the distribution of yields of Miscanthus and SRC, and the potential locations of CHP plants. The potential spatial distribution of Miscanthus and SRC energy crop areas to meet the demand is an important step in determining the ultimate distribution of these crops, when farm‐scale economics are taken into account. If the potential distribution of crops or attainable yield changes in response to environmental change, the optimal area might also change, as may the suitability of each crop compared to the other. The dynamically determined distribution of crop areas also provides a dynamic framework to estimate GHG emissions from land‐use change, as advocated by Melillo et al . ( ), rather than the use of static frameworks employed elsewhere. Energy crops are mostly grown in high yield areas, in which the production cost is relatively low. The high‐yield areas chosen reduce the deployment of energy crops on existing land with a natural ecosystem, and thus decrease potential GHG emissions from direct land‐use change (St. Clair et al ., ; Hillier et al ., ), although this will result in competition with food crops (Valentine et al ., ) and could lead to indirect land‐use change elsewhere (Fargione et al ., ). Using biomass for energy is likely to have both positive and negative competitive effects on food production and therefore on land use, although the reasons for growing crops for bioenergy are complex (Smith et al ., ). Alternatively, energy crops could be grown on land less suitable or unsuitable for food crops (Valentine et al ., ). The economics of growing crops for energy or for food are considered in Alexander et al . ( ). The distribution of energy crops has implications for low‐carbon sustainable development in the United Kingdom, which may require the systematic deployment of energy crops, rather than sparse deployment; in the United States, Heaton et al . ( ) reported that using Miscanthus to meet US biofuel goal will require less land if managed systematically than if allowed to develop unmanaged. The deployment of energy crops will result in the emission of some life‐cycle GHGs. Life‐cycle GHG emissions have a strong relationship with initial soil carbon stocks (Hillier et al ., ) and the land use they replace, as well as the agricultural management during their growth (Wang et al ., ). The cost of the life‐cycle GHG emissions will impact the final optimal deployment of energy crops (Wang et al ., ). The integration of this cost would require additional considerations: the possible ways of combining the cost include (i) setting a carbon cap for the energy crops, and/or (ii) translating the GHG cost to an economic cost, and then using the GHG emissions as a term in the partial equilibrium model. In addition to the impact on life‐cycle GHGs, the deployment of energy crops will also impact other ecosystem services. Miscanthus had higher ground flora diversity during the first 3 years after establishment compared with conventional cash crops (Semere & Slater, ), and had positive impacts on spider, beetle and earthworm diversity (Christian et al ., ). The environmental impacts of deployment of energy crops are further considered in Milner et al . ( ). Acknowledgements The study was supported by the UK Energy Research Centre (UKERC) via a UKERC/Natural Environment Research Council award: ‘Spatial Mapping and Evaluation of Energy Crop Distribution in Great Britain to 2050′ (NE/H013415/1). PS is a Royal Society‐Wolfson Research Merit Award holder.

Journal

GCB Bioenergy – Wiley

Published: Mar 1, 2014

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