Seungwoo Kang
email: seungwoo.kang@mines-paristech.fr

Sandrine Selosse

Nadia Maïzi

Methodological long-term analysis of global bioenergy potential

Keywords: Biomass Potential, Energy system, Long-term modelling, TIAM-FR

This report presents the methodology investigated in order to make more suitable and relevant the representation of bioenergy resources in the long term bottom up optimization model, TIAM-FR. Indeed, the current simplified representation is not suitable for distinguish different use for each proper bioenergy source. Furthermore, considering the important role of global bioenergy trade in energy system particularly for projecting future energy system, disaggregation of these resources appears as an essential requirement. In this study, based on the complementary purpose of improving the description of the bioenergy chain as well as the necessity of reestimation of potentials, we focused on development of energy crops and woody biomass chains rather than Industrial wastes, municipal wastes, and landfill gas, which are not currently being traded intra regions for energy uses. In the case of energy crops, otherwise, the higher disaggregation will allow apply crop-specific biofuel policies as limiting the use of edible sources for energy purpose to avoid eventual conflicts with food security issues.

1-Introduction

In TIAM-FR model, the current representation simplifies bioenergy sources and facilitates bioenergy chain formulation. More precisely, primary bioenergy sources are presented in 8 commodities; 3 types of solid biomass according to price levels (MIN-BIOSLD0, MINBIOSLD1, MINBIOSLD2), 1 type of landfill gas (MINBIOGAS0), 1 type of industrial wastes (MINBIOBIN0), 1 type of municipal wastes (MINBIOBMU0), 1 type of bio liquid (MINBIOLIQ0), and 1 type of energy crop (MINBIOCRP0). However, this aggregation level is not suitable for distinguishing different use for each proper bioenergy source. In addition, global bioenergy market is in rapid development phase not only for production for domestic consumption but also for international trade.

Table 1 shows the trade flow of bioenergy for bioethanol, biodiesel, and wood pellets from different sources and years depending according to data availability. Global bioenergy trade is estimated to be more than 2.9 EJ by 2011 and about 5% of total primary energy supply (549 EJ) in 2011. These results show the important role of global bioenergy trade in energy system and the fact that bioenergy trading is not negligible for projecting future energy system. Currently, TIAM-FR model does not include trading scheme of bioenergy but only for conventional fossil energies. For implementation of global bioenergy trade, disaggregation of bioenergy sources is an essential requirement. As regard the current aggregation level of bioenergy in TIAM-FR, different forms of woody biomass are not specified and are aggregated in one commodity, BIOSLD, despite a variety of woody bioenergy as wood pellets, fuelwood, charcoal, wood chips, and residues. Furthermore, bioenergy trade may be performed on the level of primary sources, for example, bulk roundwoods and raw crops over transformed biomass as biofuels, wood pellets. The table 1 shows also the significant share of indirect trade in global bioenergy market, which indicates exchange of raw materials for bioenergy (sugar cane itself for domestic bioethanol production, saw dusts for transforming in wood pellets and etc.). Hence, the M. Junginger et al. [START_REF] Junginger | Opportunities and barriers for international bioenergy trade[END_REF],FAOSTAT [4] FAOSTAT [4] IEA [START_REF] Bauen | Bioenergy -a Sustainable and Reliable Energy Source[END_REF] IEA [START_REF] Bauen | Bioenergy -a Sustainable and Reliable Energy Source[END_REF] FAOSTAT [4] FAOSTAT [4] IEA [START_REF] Bauen | Bioenergy -a Sustainable and Reliable Energy Source[END_REF] In this study, based on the purpose of effectuating global bioenergy trade in TIAM-FR model, and improving bioenergy chain description as well as the necessity of re-estimation of potentials, we focused on development of energy crops and woody biomass chains rather than Industrial wastes, municipal wastes, and landfill gas, which are not currently being traded intra regions for energy uses. In the case of energy crops, current rough level of disaggregation does not allow apply crop-specific biofuel policies as limiting the use of edible sources for energy purpose to avoid eventual conflicts with food security issues. For example, Indian and Chinese government implemented regulations to restrict the use of sugar cane and oil seeds. However, the current structure, which refers to one aggregated commodity of energy crops, is not capable of managing crop-specific features. Moreover, energy crops chain in TIAM-FR model links simultaneously the single energy crops commodity to both bioethanol and biodiesel conversion processes as well as to use as solid biomass (see figure 2). Then, this structure has mixed energy crop potentials for both bioethanol, which intakes sugar/starch crops and biodiesel, which intakes oil bearing crops, then, may bring unrealistic results of projection. In addition, 2nd generation of bioethanol is set to take only energy crops excluding any other cellulosic materials, for example, agricultural residues. These identified weaknesses of the current structure are addressed in this study by reformulating energy crops energy chain as well as detailed disaggregation of primary energy crops. To distinguish different types of energy crops and following conversion processes, surface-based structure is developed (see figure 3). Firstly, the total surface data is given to model, and it acts as the upper limit of the sum of surfaces of each selected crop. Secondly, the allocated surfaces for each crop are converted into energy unit with taking crop-specific productivity and assumed crop price as well as energy conversion rate, which is differentiated by water and energy content level. Thirdly, selected sugar/starch crops may be taken by followed energy transformation processes including uses as solid biomass except for 1st generation of biodiesel production. On the contrary, oil bearing crops exclude only 1st generation of bioethanol production. In addition, charcoal production process is eliminated in energy crop chain as it takes woody biomass as raw material rather than crops. This new scheme gives an endogenous choice of bioenergy crops and corresponding energy transformation process through optimizing the entire energy system cost. Also, it enables to implement bioenergy trade in raw material level as well as final energy form. Likely to energy crops, solid biomass has also a simplified and aggregated energy chain in TIAM-FR. The type of primary biomass solid (roundwood, forestry and agricultural residues) is distinguished only by assumed three price levels and aggregated in to one commodity called as "BIOSLD", which serves different conversion processes. It should be noted that this structure may bring a significant error on the available biomass solid for various conversion process and end-uses. For example, charcoal production cannot be effected from saw dusts, small wood particles, and agricultural residues, but, this structure allows using a variety of solid biomass in this process. Hence, further classification is highly required to procure more precise projection results. As results, new structure of solid biomass is proposed along with energy crops. This new feature distinguishes 4 types of solid biomass; wood supply, wood processing residues, logging residues, and agricultural residues. In terms of conversion process, wood pellet production process is newly implemented and linked to processing residues and wood supply. Also, biogas production by anaerobic digestion is introduced with taking wood supply, logging residues and agricultural residues as inputs. Charcoal production process is disconnected with previous improper sources and takes only roundwoods from wood supply. Furthermore, agricultural residues are considered as one of the sources of 2nd generation of bioethanol (cellulosic ethanol plants) production. Consequently, this figure enables now to observe wood pellets trade, which represents the largest trading commodity as well as the largest traded primary source (round woods).

These propositions of new disaggregation of bioenergy sources and modified energy conversion processes for both energy crops and solid biomass require re-evaluation of commodityspecific potential. In this study, we identified 316-435 EJ/yr of energy crops potential and 38-49EJ/yr of woody biomass potential at a global scale by 2050. Comparing with total primary energy supply level (549 EJ) in 2011, bioenergy may answer to about 90% of TPES in the world. This estimation shows the importance of bioenergy deployment in a long-term projection. In the following sections, we described the developed methodologies to estimate bioenergy potentials and following results through different scenarios.

2-Bioenergy from agriculture

Agricultural bioenergy refers to energy crops, which means sugar and starch crops, oil-bearing crops, and agricultural residues. In this chapter, global bioenergy potential assessment has been made for each region aggregated and presented in TIAM-FR model. In the world, land is limited resource and then, bioenergy production from agricultural products may bring competition with food supply. In this study, bioenergy production from agricultural lands avoids possible competition with food at most.

2-1-Methodology

To estimate bioenergy potential from agricultural products, food-first approach is applied. This approach gives a priority on responding firstly to food demand and allocating adequate land for food production. The concept of this approach has been previously applied in a study of Smeets et al [START_REF] Smeets | A quickscan of global bio-energy potentials to 2050 An analysis of the regional availability of biomass resources for export in relation to the underlying factors[END_REF]. In this study, we limited land types, which are available for bioenergy production only on "current cultivated land" and "grass and other wooded land" following by land classification of GAEZ (Global Agro-Ecological Zones, from FAO) assessment. The estimation methodology composed by several steps. Firstly, land required for food production is estimated based on food demand and population projection. Secondly, the expansion of infrastructure land (Built-up land) is estimated based on current built-up land occupation rate per capita and population projection. In our estimation, the deforestation for agricultural land expansion for both purposes of food production and bioenergy production is not allowed for all projection years. The main formula of land availability is expressed as follows:

Where:

Each type of required land estimation is described in the following sections from 2.1.1 to 2.1. .

www.cma.fr

Methodological long-term analysis of global bioenergy potential

2-1-1 -Food demands by 2050

Future food demands are estimated based on future diet evolution from FAO's recent publication "World agriculture: towards 2030/2050 Prospects for food, nutrition, agriculture and major commodity groups" (hereafter referred as "WAT") [START_REF] Alexandratos | World agriculture towards 2030/2050: the 2012 revision[END_REF] and United Nations' population projection [START_REF]World Population Prospects : the 2012 Revision[END_REF] for each country. In FAO's publication on world diet evolution, diet evolution is estimated per regional aggregates, which divide the world in "Sub-Saharan Africa", "Near East/North Africa", "Latin America and the Caribbean", "South Asia", "East Asia" and "Developed countries". Hence, to estimate future food demand per country, diet evolution per commodity groups was firstly disaggregated into country level and multiplied by population projection. In addition to region disaggregation, it is required to disaggregate the commodity groups presented in FAO's diet evolution estimation. All food commodities were aggregated in 8 groups as cereals, roots and tubers, sugar and sugar crops, pluses, vegetable oils, meat, milk and dairy, and other food. And these aggregated food commodity groups were disaggregated based on current composition ratio of food consumption published in food balance sheet (FBS) of FAO [4]. The general methodology to estimate total food consumption quantity is expressed as follows:

; / -$M " N $ " $ " " " N $ ( * %" " $ " " Where:

Along with general estimation methodology, food self-sufficiency ratio is applied at the end of the food demand estimation to derive domestic production of each commodity. Based on FBS statistics, selfsufficiency ratio (SSR) is calculated from production quantity, import quantity, and export quantity of base year and assumed to be constant to time horizon. The equation of SSR estimation is referred from FBS handbook and expressed as follows [START_REF] Kelly | Food balance sheets[END_REF].

In FBS, utilization of food commodity is classified in 6 categories, as food, food manufacture, feed, seed, waste, and other uses. Consumption for food purpose is the sum of food and food processing in most of commodities except for the commodity, which presented both in processed products and crops, for example, sugar with sugar cane. In this case, processed quantity from original crops is excluded and reallocation to final product is assumed. For certain processed food commodities as sugar, vegetable oils, milk and dairy, and rice, which do not correspond with general methodology, supplementary unit matching had to be complied. In the case of estimating seed and waste amount, the proportion between "seed and waste" and "food and processing" of baseyear for each region and commodity is compiled and applied to the time horizon due to the lack of information and difficulty of projecting changes in the future. However, the share of "seed and waste" mostly remains very insignificant (less than 1%) and not enough to change the picture of future demands. The specific features of our estimation are described in following sub-sections.

W Working Paper N°2016-03-21

2-1-1-1-Rice estimation

In the case of rice, annual consumption data is presented in milled equivalent while the productivity data is expressed in paddy. According to guideline of "Food balance sheet", 67% of conversion rate is assumed to estimate milled equivalent weight from paddy rice. Hence, the inversed conversion rate is applied to estimate paddy rice consumption quantity in the future.

2-1-1-2-Sugar crops estimation

Regarding sugar consumption, FBS presents sugar consumptions distinguishing sugar products and sugar crops, thus, they are estimated sugar crops in crops weight and sugar in sugar weight. On the other hand, FAO's estimation on diet evolution expressed sugar and sugar crops consumption in sugar raw equivalent unity. Similarly to rice conversion stage, conversion rates from sugar crops to sugar products, of which 11% for sugar cane and 15% for sugar beet imposed, are retrieved from FBS guideline. Hence, the total sugar consumption is disaggregated by following equation. In addition, it should be noted that the sugar consumption quantity should be recalculated by using self-sufficiency ratio, which excludes imported sugar quantity, to estimate domestic sugar crops amount used in sugar transformation. This equation expresses the estimation of sugar crops consumption from WTA's future diet evolution. In our calculation, two assumptions were made. Firstly, the proportion between the quantity of sugar crops used in processing and direct sugar consumption maintains to time horizon from base year. Secondly, the proportion between sugar cane and sugar beet used in processing remains constant as well as the proportion of direct food consumption between those two sugar crops.

/

/

www.cma.fr Methodological long-term analysis of global bioenergy potential

2-1-1-3-Oil crops estimation

In the line with sugar crops estimation, the estimation on vegetable oils and oil crops, which is made by WTA, is aggregated in one group commodity with oil equivalent unity. Hence, two disaggregation phases were developed. Firstly, the aggregated vegetable oil consumption is divided into oil and oil crops consumptions. For this phase, the proportion between oil crops consumption for food and vegetable oils consumption for food and processing, based on FBS's baseyear data, was estimated. and are the corresponding varaibles and expressed in following equations.

In addition, each oil crops data needs to be converted into vegetable oil equivalent data. In FBS' handbook, oil conversion rates for certain oil crops are presented. For the rest of oil crops, external researches [9][10] completed the list of conversion rates (See table 2). Secondly, each readjusted oil crops and vegetable oils consumption data go through the re-allocation phase for estimating individual consumption of each oil crops commodities. This reallocation process is based on each commodity share in total oil crops of baseyear.

Oil crops Conversion ratio Source

W Working Paper N°2016-03-21

2-1-1-4-Milk and dairy

Milk and dairy consumption is also aggregated into one group commodity in WTA's estimation. It needs only conversion of cream to milk because the cream consumption is limited only to direct food purpose. 35% of Cream extraction rate from milk is retrieved from the guideline on the preparation of supply/utilization accounts (SUAs) published by FAO [START_REF]Food Balance Sheets: A Manual[END_REF]. The disaggregation steps are expressed in the following equations. In the first step, cream consumption of base year is converted to milk equivalent unit using cream extraction rate to estimate aggregated milk and dairy consumption. In the second step, the future demand of each commodity is calculated by using the proportion between cream and milk consumption of base year.

2-1-2-Projection on feed demand by 2050

Recently, the increasing demands of meats products became one of the major drivers for agricultural land use. In addition, the growing livestock productions lift up the agricultural production as well as corresponding land uses.

According to FAO's recent report [START_REF] Alexandratos | World agriculture towards 2030/2050: the 2012 revision[END_REF], livestock production is identified as the world largest land user by grazing and production of fodder and feed grains. On the other hand, required land for livestock products depends on the efficiency of production and production system. Currently, no global database as FAOSTAT provides such data in global coverage. However, some studies [START_REF] Smeets | A quickscan of global bio-energy potentials to 2050 An analysis of the regional availability of biomass resources for export in relation to the underlying factors[END_REF], [START_REF] Bouwman | Exploring changes in world ruminant production systems[END_REF] attempted to identify these parameters either to explore the evolution of livestock production system or to estimate bioenergy potentials. In these studies, three different ways of livestock production were proposed as pastoral system, which grazes livestocks mostly from pastures and fodder crops, landless system, which feeds the animals only from feed crops and residues, mixed system, which combines those two systems. Also, three different feed compositions, which are "grasses and fodder", "feed crops", and "residues", are identified. In order to estimate final demand of animal feed for each type, the following equation, which was previously developed by Smeets [START_REF] Smeets | A quickscan of global bio-energy potentials to 2050 An analysis of the regional availability of biomass resources for export in relation to the underlying factors[END_REF], was reformed in this study.

Where Feed is the final estimation of each animal feed type i, of which 3 compositions are described above, for projection year and for specific region. is the meat demands estimated from previous section for meat type j, which includes meat, milk, mutton and goat meat, pig meat, and poultry meat, for projection year and specific region. is the feed composition for each meat type i and feed type j for production system "prod". This parameter is the share of each feed category in total demand for animal feed. is the feed conversion efficiency for each meat type and production system and expressed in the amount of feed demands (kg DM) per the amount of animal products production (kg animal product).

In our study, we included two livestock systems, i.e. "Landless" and "Mixed" system. "Pastoral" system is excluded under estimation of technological progress and also by the reason that pastoral system has too low land/product efficiency to fulfil the fast growing demand of meats in the future. The data on feed composition is collected from two previous studies [START_REF] Smeets | A quickscan of global bio-energy potentials to 2050 An analysis of the regional availability of biomass resources for export in relation to the underlying factors[END_REF], [START_REF] Bouwman | Exploring changes in world ruminant production systems[END_REF] (see table 3). In case of feed conversion efficiency, mixed and landless systems are assumed to have the same feed conversion efficiency due to the saturation of efficiency improvement. In our study, the highest level of feed conversion efficiency, which was estimated in [START_REF] Bouwman | Exploring changes in world ruminant production systems[END_REF] for 2030, is applied to the end of time horizon for all regions and for both landless and mixed system. In final steps to estimate feed demands, disaggregation of each feed type to detailed commodity was addressed instead of estimating aggregated feed products yields. Firstly, it is assumed that grasses and fodder crops are composed of two commodities as alfalfa and grass. Actually, natural pasture grazing is extremely complicated to estimate its productivity due to the large variation between localizations. Furthermore, data on pasture grazing at a global coverage is not available. Hence, alfalfa and grasses are treated as main fodder crops in this study. Nowadays, alfalfa is being considered as more attractive fodder crops due to their low moisture content compared to grasses. The dry matter conversion factors are estimated about 0.89 for alfalfa and 0.2 for normal grasses [START_REF] Steen | Beef cattle feeding and nutrition[END_REF][START_REF] Reiling | Feed Dry Matter Conversions[END_REF][START_REF] Ccof | Average dry matter percentages for different livestock feeds[END_REF]. A fraction of composition between alfalfa and grass is assumed and applied except for the regions where alfalfa production does not appear, in this case, fodder crops contain only grasses. Secondly, detailed commodities in grouped feed crops are estimated based on food balance sheet of base year. The composition and share of each feed crop are maintained to the time horizon.

PROD

2-1-3-Projection on crops yields by 2050

In this study, we used the projection of crops yields from GAEZ assessment of the International Institute of Applied Systems Analysis (IIASA). GAEZ assessment provides the estimation on crops yields through different scenarios and different constraints. Firstly, agro-climatic yields are calculated in GAEZ biomass model, which reflects climatic constraints as temperature, radiation and moisture regimes as well as water stress regarding to crop production. Secondly, agro-ecological yields include soil fertility beyond climatic potentials. In addition, each constraint interacts with different emission scenario with regard to IPCC's scenario from A1 to B2. In our study, we assumed primarily A1 scenario, whose storyline describes a future world with fast economic growth and rapid introduction of new and efficient technologies. In the line with our assumption, corresponding socio-economic parameters, for example, demography profiles projected by United Nations [START_REF]United Nations, World Population Prospects: The 2012 Revision. Highlights and Advance Tables[END_REF] were collected and applied. In the case of produc-tivity constraints, agro-climatic yields were used in priority and agro-ecological yields completed the future productivity for the crops, of which attainable yields are not contained in GAEZ assessment. Nevertheless, soil nutrient quality is not ignored but accounted in land availability calculations by limiting further land expansion on "grasses and other wooded land" following the level of soil nutrient quality. The land calculation phase will be explained in details in next section. In GAEZ model results on crops productivity, two different water systems are distinguished; "rain-fed" and "irrigation". Hence, the combination of these two productivities are realized under a fraction estimation between rain-fed and irrigation system. Furthermore, GAEZ model projects future productivity evolution for 2020 and 2050.

As future food demands are projected for 2030 and 2050, the linear interpolation between 2020 and 2050 is included in our calculation for estimating the crops yields on 2030. As a result, following equation is developed to derive corps yields for each region and projection year.

; /

N !$$ " * " * N !$$ " * * N " " $$ $ N " $$ $
Also, detailed information on crops yields data applied in this study is described in table 5. This table shows the commodity matching notes between future food demand and available crops yields data and the status of data existence on GAEZ assessment.

www.modelisation-prospective.org

2-1-4-Projection on land availability by 2050

In this study, the land classification of GAEZ assessment, which divides the land cover in cultivated land, grass land, built-up area, forest land, and other non-productive land (barren and sparsely vegetated), was applied. By using food-first approach, required land use for food and feed demand is firstly allocated to the total surface of cultivated land and grass land. Then, the rest of grass land is considered as suitable for bioenergy crop production. Current forest land is completely conserved from other uses. In addition, the protected area on grass land is also conserved in our estimation for the aspect of sustainable development. In the first step, built-up area is projected to time horizon for each region. Based on land occupation per capita of reference year, future built-up area is calculated by multiplying demographic information. Then, the expansion of built-up area spanned to grass land and barren land following the proportion between two surfaces of reference year.

In the second step, the change in agricultural land is estimated for each region and projected year using crops yields and food demands. In this case, extension is allowed only on current cultivated land and grass land.

! " "

Also, fodder land required for animal products is estimated for each region using fodder crops demand data and corresponding fodder crops yields. Fodder land use is allocated only on grass land.

As results, first estimated land availability becomes the rest of grassland after extracting the surface amount required for animal feeds and food production and can be expressed as follows.

www.modelisation-prospective.org

W Working Paper N°2016-03-21
However, as we applied crops yields under agro-climatic constraints not taking account of soil qualities, soil nutrient quality needs to be addressed for final estimation of land availability. In this context, the classification on soil nutrient quality of GAEZ assessment data was used to filtering once again our first estimated land availability. Soil quality level is defined from very severe for the least nutrient level to no constraint for abundant nutrient level.

A range from severe to no constraint is applied in our calculation for the purpose of avoiding over-estimation on land availability.

Hence, along with grass land occupation from agricultural land expansion, fodder crops production, and built-up area expansion, low nutrient level area is extracted to estimate final available land for bioenergy production. The final land availability can be expressed as following diagram and equation.

www.cma.fr

Methodological long-term analysis of global bioenergy potential

On the other hand, the countries in boreal zones required further treatment, because GAEZ assessment defined that soil quality in this area is evaluated not suitable for vegetation. Then, it resulted that the surface total, of which nutrient quality ranges from server to no constraint, is much less than the sum of cultivated land, forest land, and grassland. To avoid this underestimation, forest areas in boreal zone are added to complete total fertile surface. Following the assumption of no-forest area use for agricultural purpose, the total area of other wooded land in boreal zone was added to total nutrient surface for Russian federation, Alaska in US, Canada, Norway, Sweden, and Finland. The forest resource data of the boreal countries was collected from FAO's publication [START_REF] Dickerson | Boreal forests[END_REF].

Forest category (million ha

2-1-5-Agricultural residues

Agricultural residues are calculated based on region and crop-specific RPR (residue to product) ratio. RPR ratio is composed of two elements, firstly, harvest factor to estimate total crop residues, secondly, recovery factor to estimate collectable crop residues. Region and crop-specific RPRs are mainly retrieved from literatures [START_REF] Krausmann | Global patterns of socioeconomic biomass flows in the year 2000: A comprehensive assessment of supply, consumption and constraints[END_REF], [START_REF]Economy-wide Material Flow Accounts (EW-MFA)[END_REF] and completed by SERI studies [START_REF] Jölli | Unused biomass extraction in agriculture , forestry and fishery[END_REF] for the missing elements. Agricultural residues are calculated by multiplying harvest factors and recovery rate to the amount of primary production for each region and crop as expressed in the following equation. The table 7 shows an example of harvest factor and recovery ratio applied in this study. For some commodities, region specific data is not available, then, global average is used. On the other hand, the removal of crop residues from soil is being argued because of the risk to deplete soil organic matter, degrade soil quality and fertility and soil erosion. Regarding to such environmental risks listed above, the maximum removal of crop residues should be avoided. According to our literature review on sustainable use of crop residues [START_REF] Smeets | A quickscan of global bio-energy potentials to 2050 An analysis of the regional availability of biomass resources for export in relation to the underlying factors[END_REF][21] [START_REF] Fischer | Assessment of biomass potentials for bio-fuel feedstock production in Europe : Methodology and results[END_REF][23] [START_REF] Yamamoto | Evaluation of bioenergy resources with a global land use and energy model formulated with SD technique[END_REF] , 25% or 50% of general crop residues could be removed without interfering environmental performance and 5% of recovery rate is assumed to be sustainable for vegetables and fruits. Hence, two upper bounds of residues recovery rate (25% and 50%) were set to 50% for general crops and 5% for vegetables and fruits over region and crop specific recovery rates. In the case of bioenergy crops, the potential surplus land has been estimated for different scenarios. The tested scenarios consist of different proportion level of irrigation and rain-fed system levels, share of alfalfa in fodder crops, and animal production systems; landless and mixed system. Particularly water system evolution, which implies increas-ing share of irrigation system in cultivation, is limited to 80% of total cultivated land except for the countries, which currently equipped more than 80% of irrigation system; in this case, current irrigation system share is maintained to the time horizon. The water system of cultivation is set to evolve from each country's current share of irrigation system. Data on water system of each country, 'Area equipped for irrigation and percentage of cultivated land', was collected from AQUASTAT [START_REF]Food and Agriculture Organization of the United Nations (FAO[END_REF] of FAO for the reference year. First scenario set is considered as maximum technology evolution which implies 40% of increase in irrigation system until 2050, 80% of alfalfa in fodder crops (2) , and landless or mixed animal production system. Then, second scenario set decreases the irrigation system evolution to 30% and third scenario set to 20%. Each scenario set is calculated for two different animal systems (mixed and landless) along with the assumption of 80% alfalfa share in fodder crops. 7), Scen 1-1 showed the largest potential surplus agricultural land in 2050 of 3.1 Mha and land availability gradually decreases for the rest of scenarios up to 2.3Mha. The results show that animal production system largely effects on agricultural land demand in the Middle East Asia (MEA), India (IND), Japan (JPN), and Africa (AFR). Comparing two scenarios, scen 1-2 and scen 3-1 with moderate water system evolution and different animal production system, those regions state a sharp drop in available surface for bioenergy production to 115% for MEA, 228% for IND, 76% for JPN, and 67% for AFR. These results are originated from low pasturage productivity, less eligibility of alfalfa production, and exponential increase in demands of animal products with diet and population evolutions. According to our new structure of bioenergy scheme in TIAM-FR model, available surplus agricultural land data will be directly entered to model and new energy unit conversion processes convert each surface for internally chosen commodities. However, in this report, bioenergy potential from energy crops is simply pre-estimated based on available surface, global average of productivity (7.5 -12.6 tDM/ha) [START_REF] Coelho | Land and Water: Linkages to Bioenergy[END_REF][START_REF] Shubert | Future Bioenergy and Sustainable Land Use[END_REF], and gross calorific value of 18.3MJ/kgDM [START_REF] Krausmann | Global patterns of socioeconomic biomass flows in the year 2000: A comprehensive assessment of supply, consumption and constraints[END_REF]. Figure 8 shows bioenergy potential from energy crop production on surplus agricultural land by 2050. This graph illustrates bioenergy potential from middle level scenario, scen 3-1 (30% increase of irrigation system with mixed animal production system) and also the minimum and maximum level among 6 developed scenarios. As shown in table 10, bioenergy potential from M.Hoogwijk, 2005 achieved about 657EJ/yr under A1 scenario while our study estimated it in the range from 316-435EJ/yr. However, energy crops productivity applied in M.hoogwijk,2005 study (10-20MJ/kg for surplus agricultural land and 1-10MJ/kg for degraded land) is higher than our estimation (a constant average of 7.5MJ/kg). Furthermore, considering that our bioenergy potential will be directly introduced in surface data with region and crop specific productivity projections, final bioenergy potential could be similar to the results of M.Hoogwijk.

Item

Set

Study

316-435EJ/yr

Table10. Overview of litterature review on bioenergy potential from energy crops. (adopted from IIASA [START_REF] Coelho | Land and Water: Linkages to Bioenergy[END_REF], modified by author)

In the case of agricultural residues, energy potential from this source is estimated based on future food demands. Hence, total crops production and available residues may change by the type of animal production system, for example, landless animal production requires more feed crops by replacing pasture and grasses with feed and fodder crops. Furthermore, different limits of recoverability fraction may result differences in bioenergy potential from agricultural residues. As the projection on food demands remains constant regardless of crops yields and animal grazing system, two levels of recoverability fraction and two animal production systems derived four scenarios to estimate bioenergy potential from agricultural residues as described in table 11. As results, world bioenergy potential from agricultural residues is estimated in the range from 39 EJ/yr (Scen4) to 111 EJ/yr (Scen1). These results may be compared with 49-69EJ/yr by Smeets et al. [START_REF] Smeets | A quickscan of global bio-energy potentials to 2050 An analysis of the regional availability of biomass resources for export in relation to the underlying factors[END_REF], 50EJ/yr (technical) by IIASA [START_REF] Gea | Energy resources and potentials[END_REF] and 10-32EJ/yr by M.Hoogwijk [START_REF] Hoogwijk | Exploration of the ranges of the global potential of biomass for energy[END_REF]. Differences in bioenergy potentials from agricultural residues mainly come from different assumptions on residue to product ratio (RPR) and recovery fraction (RF). For example, the agricultural residues estimated by M.Hoogwijk applied 25% usability fraction of the total available agricultural residues as well as Smeets et al. Among our scenarios, scen3 and scen4 applied 25% of usability ratio, then, agricultural results are estimated between 39EJ/yr and 55EJ/yr, which are not dissimilar with those estimations.

No. RF limit Animal production system Results

Scen1

3-Bioenergy from forestry

Historically, forestry biomass has been widely used in the world. The consumption of forestry biomass consists of two types, firstly, traditional use for cooking and heating, secondly, modern use for transformation into biofuels and compacted woody biomass by increasing energy contents. However, wood logging from forest may derive a serious deforestation and corresponding climate change from reducing carbon stock capacity of forest. Hence, in this chapter, we tried to verify the maximum forestry bioenergy potential without degrading the environment.

3-1-Methodology

In this study, total woody biomass potential is limited to the sustainable surplus forest supply, for which supply side and demand side were separately estimated. In the case of supply side, 3 different types of supply; wood supply from (1) forest, (2) other wooded land, and (3) trees outside forest, are included in our calculation. In demand side, industrial roundwood consumption is estimated. The total surplus forest supply estimation can be expressed as equation xxx.

Surplus wood supply = Supply from forest + supply from other wooded land + supply from TOF industrial roundwood demand Lastly, forestry biomass potential becomes the sum of surplus wood supply, logging residues, processing residues, and wood products residues.

The following sections describe the methodology applied for different types of woody biomass as well as demand projection.

3-1-1-Woody biomass from forest and other wooded land

To estimate sustainable potential of woody biomass, GAI (Gross Annual Increment) is estimated for each region. FAO defines GAI as "Average annual volume of increment over the reference period of all trees, measured to a minimum diameter breast height (d.b.h.) of 0 centimetres (cm) including the increment on trees which have been felled or die during the reference period". Hence, this indicator allows estimating annual natural forest growth and limiting exploitation of woody biomass not more than natural growth level. Currently, FAO includes only NAI (Net annual increment), which excludes felled amount of trees from GAI, in forest resource assessment. Furthermore, FAO does not provide NAI for different categories of wood land type (forest, other wooded land, and TOF). Therefore, GAI for each region and each type of wood land needs to be re-estimated using growing stock, forest surface, wood removal, and dead wood stock data. GAI and NAI estimations followed the next equations [START_REF] Pisarenko | Development of forest resources in the European part of the Russian Federation[END_REF] . In the case of the Natural losses, none of statistics provides proper information at global coverage. Instead, deadwood stocks for each region and landtype are available to replace natural losses data, which can be considered as mortality (death of trees through natural tree death, insect attacks, fire, wind-through or other physical damage). This assumption, made in our study, has been also used in other literature to derive GAI of forests [START_REF] Pérez-Soba | Sustainability Impact Assessment of Land Use Changes[END_REF]. Moreover, total wood removals needs to be separately calculated for each land type, while FAO's data on wood removals are not distinguished according to land types. Hence, a methodology to allocate wood removals to different land types, which are "Forest" and "Other wooded land" is developed. In details, FAO defines that forest is a Land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ, and that other wooded land is Land not defined as "Forest", spanning more than 0.5 hectares; with trees higher than 5 meters and a canopy cover of 5-10 percent, or trees able to reach these thresholds; or with a combined cover of shrubs, bushes and trees above 10 percent. Also, other wooded land can include areas of shrubs and bushes where no trees are present. Hence, In this methodology, we assumed that the industrial roundwood comes mostly from forest rather than from other wooded land based on wood/forest characteristics. On the other hand, fuelwoods can be exploited from both "forest" and "other wooded land", then, fuelwood removal is allocated to each land type based on the proportion of surfaces.

In addition, to distinguish economic potential and technical potential, the share of commercial species of total growing stock is applied during GAI calculation. [START_REF] Shubert | Future Bioenergy and Sustainable Land Use[END_REF] The calculated GAI (unit : m3/ha) either commercial and technical is multiplied by each surface to estimate total woody biomass supply in volume. Then, Surplus wood supply is combined with region-specific BCEF (Biomass conversion and expansion factor) for growing stock, which is published by IPCC and FRA 2010 report. BCEF allows converting wood volumes to above ground biomass in dry matter basis weights.

3-1-2-TOF (Trees outside forests)

Apart from woody biomass from forest and other wooded land, TOF is an important source of woody biomass supply, while the assessment on TOF still suffers from insufficient data, and only few countries are providing corresponding information. Recently, FAO recommends the country members to include TOF information in FRA2015 [START_REF] Fao | Global Forest Resources Assessment[END_REF] country reporting and also realized a study on TOF assessment [START_REF] Gauthier | TO-WARDS THE ASSESSMENT OF TREES OUTSIDE FORESTS[END_REF].

In FRA 2015, a part of TOF potential is assessed for some countries regarding to "other land with trees cover". However, the number of countries that reported TOF information remains still insufficient According to FAO's definition on "other land with trees cover", wood supply from TOF in the range of 5 and 10% of canopy cover is excluded as well as trees on other wooded land.

Figures 10 and11 show different decision trees of other land with tree cover and other land with TOF.

In the decision tree for other land with TOF, wood supply from OLwTOF (NON A/U -2) and OLwTOF (NON A/U -1) are not included in other land with trees cover, but allocated in other land without tree cover or other wooded land. They visualize the threshold of each forestry land type and its classification. Based on these figures, we can identify that other land with tree cover is fully embedded in other land with TOF.

In addition to data on TOF from "FRA 2015" and "Towards the Assessment of Trees outside Forest", literature review was addressed to complete the dataset. For example, TOF information of China is not available in both of those two major sources even though the large potential of TOF. Chinese government defined TOF as four side trees, which refers to trees growing and distributed along the sides of the houses, roads, rivers, and cropland. Then, the corresponding data is directly extracted from the national forest inventory of China [START_REF]of the People's Republic of China, Forest Resources in China[END_REF].

To finalize the estimation of wood supply from TOF, surface data needs to be combined with wood production yields. FAO's definition on TOF proves that land and woods classification criteria for land with tree cover matches to the characteristics of forest land (see figure 12). Hence, region-specific GAI data on forest land are used to derive woody biomass potential on land with tree cover and to conserver sustainable level of wood exploitation. According to a study of S.Schelle et al [START_REF] Schnell | The contribution of trees outside forests to national tree biomass and carbon stocks-a comparative study across three continents[END_REF], wood productivity on other land with TOF is identified as similar with growth rate in other wooded land. Also, the tree classification thresholds states that land and tree characteristics correspond to trees on other wooded land. Currently, other land with TOF data is not available for all countries. Only for the countries where TOF data is available, the remaining surface after extracting the surface of land with tree cover is multiplied with region specific GAI data on other wooded land to estimate woody biomass potential (see equation on TOF).

; / " * $ N " ! : * $ N

3-1-3-Wood residues

Wood residues are composed of 3 elements as logging residues (harvest residues), processing residues, and wood wastes. To estimate these 3 wood residues, residues estimation methodology of E.M.W. Smeets [START_REF] Smeets | Bioenergy potentials from forestry in 2050: An assessment of the drivers that determine the potentials[END_REF] are applied. Firstly, for logging residues, wood removals data is combined with logging residues generation ratio and recoverability ratio. Due to the lack of regional specific data, global average ratios are widely adopted. Secondly, processing residues are assumed to be produced only with industrial roundwood consumptions because of the difficulty to distinguish industrial fuelwood productions with direct use of fuelwood. Similarly, wood wastes from discarded wood products ad tertiary residues were assessed using wood product generation ratio and wood wastes residue recoverability fraction. The detailed methodology is expressed as follows.

www.cma.fr Methodological long-term analysis of global bioenergy potential

3-2-Results: Forestry biomass

Forestry biomass potential is estimated from different wood supply sources as surplus forest growth, wood from TOF (Trees Outside Forest), and forestry residues based on the methodology mentioned in previous sections. It should be noted that fuelwood consumption is excluded from wood demand prospective and considered as a source of bioenergy, then, included in final forestry bioenergy potential. In the case of wood supply potential, two different scenarios are developed, which are technical potential on the one hand, and economic potential on the other hand. The difference in those two scenarios comes from different GAI indices. In details, GAI results are applied in order to derive technical potential of wood supply and GAI results combined with commercial growing stock rate in total growing stock are applied for economic potential. First of all, sustainable wood growth is calculated as the range of 101.85EJ/yr for technical potential and 55.45EJ/yr for economic potential (see table xxx). The difference between two scenarios comes from different commercial species share in total wood species and following GAI data. Under technical potential estimation, global average GAI data are estimated to 2.91 m3 /ha/yr for "forest" and 1.98 m3/ha/yr for "other wooded land". However, these estimations decreased with excluding non-commercial volumes to 1.91 m3/ha/yr for "forest" and 1.12 m3/ha/yr for "other wooded land". Our estimation on sustainable wood growth is in the line with wood supply potential from forest of 103EJ (with global GAI of 3.4 m3/ha/yr) for technical potential and 45 EJ/yr (with global GAI of 2.1m3/ha/yr) for economic potential estimated by E.M.W. Smeets [START_REF] Smeets | Bioenergy potentials from forestry in 2050: An assessment of the drivers that determine the potentials[END_REF]. Including wood supply from TOF, forestry residues and wastes, final forestry biomass potential by 2050 is derived as 114EJ/yr for technical potential and 68EJ for economic potential, of which residues and wastes contribute 18.4 EJ/yr. The forestry residue estimation is in the line with other studies, 19-35EJ/yr by IIASA [START_REF] Gea | Energy resources and potentials[END_REF] and 28 EJ/yr by Smeets [START_REF] Smeets | Bioenergy potentials from forestry in 2050: An assessment of the drivers that determine the potentials[END_REF]. However, it should be noted that fuelwood demand is included in these estimations under assumption that it would be an internal choice of optimization results from TIAM-FR. On the other hand, applying fuelwood demands projected by M.W.Smeets in the range of 20EJ -30EJ, our final estimation becomes 84EJ -94 EJ for technical potential and 38EJ/yr -48 EJ/yr for economic potentail. These results may be compared with other studies, for example, 90-115EJ of technical potential estimated by Fischer [START_REF] Fischer | Global bioenergy potentials through 2050[END_REF], 91.9 EJ/yr of technical potential and 42.5EJ/yr of economic potential estimated by E.M.W. Smeets and 12-74EJ by IPCC AR4 [START_REF] Nabuurs | Forestry[END_REF].

Country

Figure 1 .

 1 Figure 1. Global bioenergy trade flow (adopted from IEA bioenergy [1])

Figure 2 .

 2 Figure 2. Overview of current energy crops chain in TIAM-FR

Figure 3 .

 3 Figure 3. New structure for energy crops chain

Figure 5 .

 5 Figure 5. New structure of solid bomass chain

Figure 6 .

 6 Figure 6. Land classification and brief in future land allocation

Figure 7 .

 7 Figure 7. Potential surplus agricultural land by region in 2050

Figure 8 .

 8 Figure 8. Agricultural bioenergy potential by 2050

18 Figure 9 .

 189 Figure 9. Bioenergy potential from agricultural residues by 2050

Figure 10 .

 10 Figure 10. Decision tree for other land with TOF (cited from FAO)

Figure 11 .

 11 Figure 11. Decision tree for other land with tree cover (cited fromFAO)

Figure 13 .

 13 Figure 13. Total forestry bioenergy potential by 2050 (technical potential)

Figure 14 .

 14 Figure 14. Total forestry bioenergy potential by 2050 (economic potential)

Table 2 .

 2 Oil conversion rate for oilcrops

	Palm	0.225	GAEZ assessment
	Soybean	0.17	Gressen
	Sunflower	0.44	Gressen
	Rapeseed	0.4	Gressen
	Jatropha	0.38	Gressen
	Groundnut	0.45	FAO
	Cotton	0.18	FAO
	Olives	0.15	FAO
	Coconuts	0.123	FAO + own calculation using copra and copra oil conversion
	Sesame and others	0.3	Average data

Table 3

 3

		Animal Products	Grasses/Fodder	Feed Crops	Agri Residues
		Bovine	0	0,8	0,2
		Dairy products	0	0,8	0,2
	LANDLESS	Mutton and goat	0	0,75	0,25
		Pig	0	0,75	0,25
		Poultry	0	0,75	0,25
		Bovine	0,5	0,3	0,2
		Dairy products	0,5	0,3	0,2
		Mutton and goat	0,85	0,1	0,05
		Pig	0	0,75	0,25
		Poultry	0	0,75	0,25

. Feed composition for each animal product MIXED www.cma.fr Methodological long-term analysis of global bioenergy potential

Table 4 .

 4 Feed conversion efficiency (kgDM feed/kg animal product)

Table 5 .

 5 Detailed information on application of productivity data www.cma.fr Methodological long-term analysis of global bioenergy potential

Table 6 .

 6

	)	Former USSR	Alaska	Canada	Norway	Sweden	Finland
	Forest and other wooded land	790	46	327	7	21.4	22.7
	Closed forest	673	5	198	5.9	18.4	19.5
	Exploitale closed forest	450	5	144	5.1	16.1	19

Major categories of forest land within countries and within their boreal zones (FAO)

Table 8 .

 8 ScenariosAs results (see table9 and figure

	no.	Scen no.	Water system	Animal production system	Alfafa vs grass
	Set 1 :	Scen. 1-1	+40% in 2050	Landless
	Maximum tech	Scen. 1-2	+30% in 2050	Landless
	Set 2 :	Scen. 2-1	+40% in 2050	Mixed	80% for alfalfa
	Medium tech	Scen. 2-2	+20% in 2050	Landless	20% for grass
	Set 3 :	Scen. 3-1	+30% in 2050	Mixed
	Low tech	Scen. 3-2	+20% in 2050	Mixed

Region Scen 1-1 Scen 1-2 Scen 2-1 Scen 2-2 Scen3-1 Scen 3-2

	W Working Paper N°2016-03-21
	MEA	87	85	79	0	0(-13)	0(-22)
	USA	229	224	214	219	211	206
	JPN	3	3	3	1	1	1
	EEU	35	35	34	34	33	33
	CHI	247	243	235	235	228	223
	CSA	403	401	395	403	398	395
	AUS	449	447	442	454	449	447
	FSU	678	674	662	642	632	626
	WEU	60	58	53	59	55	52
	IND	3	0(-4)	0 (-19)	5	0(-12)	0(-23)
	ODA	71	68	62	59	54	51
	SKO	1	1	1	1	1	1
	MEX	80	79	77	72	69	67
	AFR	656	649	631	328	215	77
	CAN	174	173	171	175	174	174
	World	3175	3136	3041	2687	2494	2307
	www.modelisation-prospective.org

Table 9 .

 9 Potential surplus agricultural land in Mha for 2050

Our study Technical Global 2030-2050 Surplus agricultural land (100 %), Grass and other wooded land (limited to soil quality)

		Type of potential	Regions	Time frame	Land use types	Potential
					Land suitable for
					bioenergy cultivation
	WBGU, 2008	Technical	Global	2050	according to the crop functional types in the	34-120EJ/yr
					model, considering
					sustainability
	Smeets et al, 2007	Technical	Global	2050	Surplus agricultural land (100%)	215 -1272 EJ/yr
					Surplus agricultural
	Hoggwijk et al.,2003	Technical	Global	2050	land, Surplus degraded	8 -1098EJ/yr
					land
					Abandoned agricultu-ral land (100%), Re-	Total : 311-657
	Hoggwijk et al.,2005 [29]	Technical	Global	2050-2100	maining land not for food or material pro-	EJ/yr(Climate scenario A1 :
					duction (10-50%), Extensive grassland	657EJ/yr)
					Cropland not needed
	Erb et al., 2009	Technical	Global	2050	for food and fiber sup-ply intensification of	28-128EJ/yr
					grazing land

Table 11

 11

. Agricultural residue scenarios and results www.cma.fr Methodological long-term analysis of global bioenergy potential

Table 12 .

 12 Share of commercial species from FRA2010

	www.modelisation-prospective.org

Table 13 .

 13 Biomass conversion and expansion factor from FRA2010[START_REF] Fao | Global Forest Resources Assessment 2010[END_REF]

	www.cma.fr

Table 14 .

 14 Technical Economic Country Technical Economic Sustainable wood growth by 2050

	UNIT: EJ		Sustainable wood growth
	MEA	0.70	0.37	WEU	7.05	7.02
	USA	12.37	11.16	IND	14.99	4.32
	JPN	1.68	0.56	ODA	5.26	1.56
	EEU	4.23	4.20	SKO	0.52	0.17
	CHI	4.75	1.60	MEX	0.13	0.10
	CSA	11.08	2.76	AFR	20.94	5.54
	AUS	2.30	1.18	FSU	7.94	7.86
	CAN	7.93	7.05	World 101.85	55.45

www.modelisation-prospective.org

(2) Alfalfa share imposition is applied only for the countries, which currently have alfalfa production. It is assumed that alfalfa production is not eligible for non cultivating countries in 2010.

Acknowledgements

This research was supported by the Chair Modeling for sustainable development, driven by MINES ParisTech, Ecole des Ponts ParisTech, and AgroParisTech, supported by ADEME, EDF, GRTgaz, SCHNEIDER ELECTRIC and the French Ministry of Ecology, Sustainable Development and Energy. www.cma.fr Methodological long-term analysis of global bioenergy potential

bioenergy potential Bioenergy from forestry 3-1/ Methodology 3-1-1/ Woody biomassfrom forest and other wooded land 3-1-2/ TOF (Trees outside forests)

-PR : Processing residues -CI : Industrial roundwood consumption -P : processing residue generation ratio (set at 0.5) -Pr : processing residue recoverability ratio (set at 0.75) -D, HV : same as logging residues

Wood wastes • DW = CI x w x wr x D x HV [EJ/yerar] • -CI : Industrial roundwood consumption -W: wood product generation ratio (set at 0.5) -Wr : Wood waste recoverability ratio (set at 0.75) -D, HV : same as logging residues

3-1-4-Wood demand projection

In this study, fuelwood consumption is considered as one of the bioenergy sources unlike to other literatures. The choice of fuelwood remains to the optimization results via the energy system model TIAM-FR. Hence, wood demands are projected only for industrial round wood. Currently, wood demand projection is hardly available for region-specific data and if available, region definition differs from TIAM-FR. According to literature review, the world total demand of industrial roundwood is projected to normally be in the range from 0.9Gm3 (10 EJ) to 3.1 Gm3 (36EJ) (3) [START_REF] Smeets | Bioenergy potentials from forestry in 2050: An assessment of the drivers that determine the potentials[END_REF][START_REF] Brown | The global outlook for future wood supply from forest plantations[END_REF][START_REF] Sohngen | An Analysis of Global Timber Markets[END_REF] and is not dissimilar with simple projection using current consumption per capita level. Thus, Industrial roundwood demands are estimated based on constant region specific ratio of consumption per capita and demographic evolution from UN. As results, Industrial roundwood demand in world is estimated to about 23 EJ by 2050, and it remains on the level of projection from other studies.

(3)

In extreme case, industrial roundwood demand reaches to 80EJ by 2050 (Sedjo and Lyon, 1998 [START_REF] Sedjo | Timber Supply Model 96 : A Global Timber Supply Model with a Pulpwood Component[END_REF])


Deep-UV 236.5 nm laser by fourth-harmonic generation of a single-crystal fiber Nd:YAG oscillator Loïc Deyra, 1, * Igor Martial, 2 Julien Didierjean, 2 François Balembois, 1 and Patrick Georges 1 We demonstrate a deep-UV laser at 236.5 nm based on extracavity fourth-harmonic generation of a Q-switched Nd: YAG single-crystal fiber laser at 946 nm. We first compare two nonlinear crystals available for second-harmonic generation: LBO and BiBO. The best results at 473 nm are obtained with a BiBO crystal, with an average output power of 3.4 W at 20 kHz, corresponding to a second-harmonic generation efficiency of 38%. This blue laser is frequency-converted to 236.5 nm in a BBO crystal with an overall fourth-harmonic generation yield of 6.5%, corresponding to an average output power of 600 mW at 20 kHz. This represents an order of magnitude increase in average power and energy compared to previously reported pulsed lasers at 236.5 nm. This work opens the possibility of LIDAR detection of dangerous compounds for military or civilian applications.

The development of deep-UV solid-state lasers based on nonlinear frequency conversion has opened applications previously based on excimer lasers, such as material processing or spectroscopy applications. The most common approach is fourth-harmonic generation of the 1064 nm laser line of Nd:YAG, which has been a commercial product for many years. Unfortunately, important applications, such as manufacturing of Bragg gratings or waveguides, would be more efficient using wavelengths below 250 nm. Moreover, some particular spectroscopic applications, such as detection of dangerous compounds, require specific wavelengths in the deep-UV. One possible solution is to use a fundamental laser operating at a lower wavelength, such as the 946 nm laser line of Nd:YAG. Subsequent fourth-harmonic generation leads to a wavelength of 236.5 nm, which lies in the absorption band of molecules related to improvised explosive devices or nuclear, radiological, biological, and chemical components [1,2]. The challenge is to obtain enough pulse energy to increase the detection range and accuracy. Unfortunately, previous works at 236.5 nm only demonstrated a maximum energy of 500 nJ [3,4], with an average power of 20 mW. This energy limitation comes from the difficulty to obtain significant laser output in Nd:YAG at 946 nm, because the quasi-threelevel transition induces strong thermal effects. Recently, we demonstrated that single-crystal fiber technology could significantly improve the available laser output at 946 nm in Nd:YAG, owing to an optimized thermal management allowed by the crystal geometry and packaging [5,6]. In this Letter, we propose to use a Nd:YAG single-crystal fiber Q-switched oscillator at 946 nm as a source for efficient fourth-harmonic generation. We show that this leads to an order of magnitude increase over previously reported results both in pulse energy and average power at 236.5 nm. The experimental setup is displayed in Fig. 1. The laser is a Q-switched oscillator at 946 nm based on a Nd:YAG single-crystal fiber [6]. It emits an average power of 9.2 W in a linearly polarized beam at a repetition rate of 20 kHz, with a pulse width of 45 ns. The beam profile is Gaussian with a measured beam quality of M 2 x 1.11 and M 2 y 1.13 (Fig. 3). The pulse energy is 460 μJ, corresponding to a peak power of 10.2 kW.

Several crystals are commercially available to perform second-harmonic generation (SHG) to the visible range. We consider three crystals: lithium tetraborate LiB 3 O 5 (LBO), bismuth borate BiB 3 O 5 (BiBO), and periodically poled titanium phosphate (PPKTP). Their material and nonlinear properties are displayed in Table 1.

LBO is the most commonly used nonlinear crystal for visible SHG. While its nonlinearity is low compared to the other two crystals (d eff 0.81 pm∕V), it has low walkoff, large angular acceptance, a very high damage threshold, and can be manufactured in large dimensions with very good optical quality. In contrast, BiBO has a large nonlinear coefficient (d eff 3.34 pm∕V), but suffers from a large walk-off and low angular acceptance, often resulting in elliptical output beam. This also lowers the conversion efficiency for tight focusing when compared to crystals with a lower walk-off value. Furthermore, BiBO is a hygroscopic crystal that cannot be exposed to ambient air for a very long period of time. Photorefractive damages have also been reported in BiBO [7]. Finally, periodically poled crystals such as PPKTP [8] benefit from a very large nonlinear coefficient (d eff 10 pm∕V), large angular acceptance as well as no walk-off, but have a low damage threshold, exhibit absorption in the visible range [9], and suffer from severe photorefractive damage (gray-tracking) at high power. Two crystals that have been used for conversion in the blue region have not been considered, namely KBO [10] and BBO [11], because they show inferior properties when compared to the other three crystals.

The choice of a nonlinear crystal is strongly dependent on the fundamental laser source. For example, PPKTP is very useful at low powers, while LBO is more reliable in long-term operation at very high powers and is not subject to photorefractive damage. We choose to eliminate PPKTP from our choice list, because our peak power value is enough to use birefringent crystals in efficient configurations.

LBO and BiBO have therefore been experimentally compared. The LBO is a 3 mm × 3 mm × 20 mm crystal cut at θ 90°, φ 19.3°for type I SHG, with antireflective (AR) coatings at 946 and 473 nm. The BiBO is a 3 mm × 3 mm × 10 mm crystal cut at θ 161.6°, φ 90°for type I SHG, with AR coatings at 946 and 473 nm.

The 946 nm laser output is collimated, and a half-wave plate (HWP) in combination with a polarizer is used to adjust the fundamental power. Another HWP is used to control the polarization state. The beam is focused into a nonlinear crystal placed in a temperature-controlled oven. Residual infrared radiation at the output of the nonlinear crystal is filtered with a dichroic mirror.

For each crystal, the focal spot size must be chosen to maximize conversion efficiency. For a nonlinear crystal with length L, and a focused beam with Rayleigh range Zr, Boyd and Kleinman defined the optimal focusing condition as [12] ξ L 2Zr 2.84.

In our case, for 20 mm LBO and 10 mm BiBO, this translates into focused beam diameters of 70 and 50 μm, respectively. Unfortunately, these values cannot be used because the corresponding input fluence would result in optical damage of the AR coatings. We therefore choose the minimum possible beam diameter, with an energy density limit of 5 J∕cm 2 on the crystals input facet.

The beam is focused down to a beam diameter 2ω0 125 μm (defined at 1∕e 2 ). The BiBO crystal allows the most efficient SHG, with a conversion efficiency of 38% resulting in a maximum output power of 3.5 W (175 μJ) at 473 nm. The LBO crystal maximum output power at 473 nm is 2.4 W with a conversion efficiency of 26%. This is expected given that LBO has a much lower d eff than BiBO. Conversion efficiencies and resulting output powers for SHG in BiBO and LBO are displayed in Fig. 2. In all cases, no damage is observed on the nonlinear crystals, and specifically no photorefractive damage is observed in the BiBO crystal. Therefore, we choose the BiBO crystal for the SHG stage since it results in a higher blue laser output power. We now focus on beam profile characterization at the output of the BiBO SHG stage.

As expected from the narrow acceptance angle of BiBO, the output 473 nm beam profile is elliptical. The beam quality at 4σ, measured with an automatic Dataray M 2 bench, is M 2 x 1.14 and M 2 y 1.11 (see Fig. 3). The pulse duration is shortened to 34 ns.

The blue beam is then filtered from the infrared with two dichroic mirrors. A wedged window is inserted in the beam path to measure the power at 473 nm incident on the second SHG stage (Fig. 1). The choice of a nonlinear crystal for frequency doubling from 473 to 236 nm is fairly straightforward, since beta barium borate (BBO) crystals and cesium lithium borate (CLBO) are the only commercial crystals with phase-matched configurations for that process. An experiment of fourth-harmonic generation from 946 to 236 nm was reported using CLBO crystals [13], but it requires cooling at -15°C, which increases the setup complexity and reduces the CLBO lifetime. Indeed, CLBO crystals are very hygroscopic and must be maintained at high temperatures [14].

BBO has a significant nonlinear coefficient (d eff 1.5 pm∕V) for frequency conversion from 473 to 236.5 nm. This should ensure large conversion efficiency even at a moderate blue peak power of 5 kW in a singlepass configuration. We therefore use a 7 mm long BBO, cut for type-I SHG from 473 to 236.5 nm at θ 57.5°. The crystal is placed in a temperature-controlled oven at 100°C to limit the nonlinear absorption effects occurring at high intensities and repetition rate [15]. It is AR-coated at 473 and 236 nm.

The elliptical output beam from the BiBO crystal is focused into the BBO crystal with a lens of focal length f 100 mm, resulting in a beam size of 200 μm in the walk-off plane and 90 μm in the other transverse plane. An AR-coated HWP is used to adjust the input polarization. The output blue and ultraviolet beams are separated using a CaF 2 prism.

The output UV power and conversion efficiency are shown as a function of input power in Fig. 4. When the beam is focused tightly perpendicular to the walkoff plane, the output average power is 600 mW at 236.5 nm and a repetition rate of 20 kHz, corresponding to an energy of 30 μJ, and a fourth-harmonic total conversion efficiency of 6.5%. The corresponding conversion efficiency from 473 to 236.5 nm is 20%, which is the best conversion efficiency reported so far for this process [3,4,13]. We observe a slight roll-off in the conversion efficiency at high power, which is expected from the combination of tight focusing and high walk-off value.

Since BBO crystals suffer from low acceptance angle (0.35 mrad.cm) and large walk-off (80.6 mrad for the UV beam), the orientation of the focused elliptical beam with respect to the walk-off plane must be carefully adjusted. Indeed, if the input elliptical beam is focused tightly in the walk-off plane, the output UV power is lowered to 440 mW.

The final UV pulse width is 27 ns and we observed an expected decrease of pulse widths by a factor close to 0.7 for each conversion stage. The beam profile in the UV was measured with a UV beam converter from Dataray and is displayed in Fig. 5. It is elliptical as expected.

The output power stability at 236.5 nm was measured for 2 h (see Fig. 6) and no signs of UV-induced degradation or pump laser power drop were observed. Power fluctuations seem to increase at the end of the stability measurement and we attribute this to the beginning of UV-induced BBO surface contamination.

More sophisticated crystal packaging, such as sealed environment, controlled atmosphere, and protection layers on the crystal facets, should improve the crystal lifetime. The optimum phase-matching temperature does not change as a function of input power, indicating that thermal effects do not induce a thermal gradient larger than the crystal thermal acceptance [16].

In conclusion, we report on a 236 nm pulsed laser based on a Nd:YAG single-crystal fiber Q-switched oscillator at 946 nm followed by a fourth-harmonic generation experiment using a BiBO and BBO combination. We obtain a stable 600 mW, 30 μJ deep-UV laser, at a repetition rate of 20 kHz, with a pulse width of 27 ns. This represents an improvement of more than 1 order of magnitude in average power and energy (30 times and 60 times, respectively) when compared to previously reported results [3]. It should be noted that previous state-of-the-art results were obtained in a much more compact, passively Q-switched experimental setup. Our laser source is suitable for spectroscopic measurements of dangerous compounds. Further possible improvements include an 885 nm pumping scheme for the 946 nm oscillator [17] and optimizing the last conversion stage with walk-off-compensated BBO [18]. Loïc Deyra acknowledges the DGA for funding his Ph.D. This work has been partially funded by the ANR through the program "UV Challenge."

Fig. 1 .

 1 Fig. 1. Experimental setup.

Fig. 2 .

 2 Fig. 2. Average power (triangles) and conversion efficiency (circles) from 946 to 473 nm with BiBO.

Fig. 3 .

 3 Fig. 3. Beam profiles and beam qualities at 946 and 473 nm after SHG in the 10 mm BiBO crystal.

Fig. 4 .

 4 Fig. 4. Average power (triangles) and conversion efficiency (circles) from 946 to 236.5 nm for BBO.

Fig. 5 .Fig. 6 .

 56 Fig. 5. Beam profile at 236.5 nm.

1

  Laboratoire Charles Fabry, Institut d'Optique, CNRS, Univ Paris-Sud, 91127 Palaiseau, France 2 Fibercryst SAS, Parc d'activité Wilson, Bât A1, 31 Rue Wilson, 69150 Dcines Charpieu, France *Corresponding author: loic.deyra@institutoptique.fr

Table 1 .

 1 Material and Nonlinear Properties of Crystals for Blue SHG (QPM = Quasi-Phase-Matched)

	Crystal	LBO	BiBO	PPKTP
	Phase-matching type	Birefringent Birefringent QPM
	Hygroscopy	Slight	Moderate	None
	Photorefractive sensitivity Very low	Reported Severe
	Visible absorption	Low	Low	High
	d eff (pm/V)	0.81	3.34	10
	Walk-off (mrad)	11.3	40	0