Dirk Burghardt

Stefan Schmid

Cecile Duchêne

Jantien Stoter

Blanca Baella

Nicolas Regnauld ++

Guillaume Touya

Methodologies for the evaluation of generalised data derived with commercial available generalisation systems

The paper investigates methodical questions on the analyses and evaluation of automated generalised maps. The maps are produced with commercially available out-of-the-box generalisation systems, in a way that every system was tested by several persons on four test cases. The requirements on the generalised maps were described by cartographic constraints in a formal way. In addition, manually generalised maps were provided to give further reference information for the tester. The analyses of the generalised maps are to be based on empirical and automated evaluation methods. The paper will present these evaluation methods in detail with objectives, related research, how the methods are realised and expected outcomes. Possible interchanges and synergies between the evaluation methods will also be described. The work published within this paper contributes to research on formal descriptions of cartographic requirements on generalised maps. It supports the development of methods for the situation and context dependent application of generalisation functionality and serves on the evaluation of existing generalisation products, to derive future research and development potential.

Introduction

This paper reports on an on-going work that takes place in the context of the EuroSDR project studying the "state-of-the-art of commercial out-of-the-box generalisation software". The aim of this project is to test commercial generalisation software systems on "benchmark" generalisation cases [Stoter et al., 2008;[START_REF] Burghardt | Investigations on cartographic constraint formalisation[END_REF][START_REF] Stoter | Oral presentation during the Workshop of the ICA Commission on Generalisation and Multiple Representation[END_REF]. Four commercial available generalisation software systems, relying on different approaches for generalisation, have been tested on four test cases or generalisation problems. A generalisation problem is a large sale source dataset provided by a National Mapping Agency (NMA) together with specifications describing the expected output of the generalisation as well as the symbolisation of the output. The specifications are expressed as a set of constraints that the generalised data should respect. The datasets have been provided by ICC (Catalonia), IGN (France), OS (Great Britain) and Kadaster (the Netherlands). [Stoter et al., 2008] describe the project in more detail. Every software system has been tested from June 2007 till February 2008 by different testers on each generalisation problem. This has resulted in about ten outputs for each generalisation problem. Apart from structured documents in which experiences of the testers were captured, the outputs are the generalised data, consisting of all output layers in Shape formats as well as the symbolised version of the layers (i.e. output maps). After the testing stage the project has now entered its evaluation stage. This paper presents the methodology for evaluating the generalised data. The evaluation of generalised data aims at answering the question of "how much automated generalisation is available in commercial software" as well as "how different are generalisation solutions for the same generalisation problem". The evaluation methodology for generalised data of the project was designed in 2007 in an initial state. Based on test evaluations with the first versions of the methodology and based on a project meeting in April 2008, where these initial experiences were further discussed, the methodology was improved and better aligned with the research questions of the project.

The next section describes the general objectives of the evaluation task on generalised data within the EuroSDR project and presents the three main evaluation questions of this task. It also introduces the evaluation methodology aiming at answering the three evaluation questions. Sections 3, 4 and 5 detail the three evaluation procedures on which this methodology relies. The paper ends with concluding remarks and perspectives in section 6.

Evaluation of generalised data within the EuroSDR project

How many outputs have we got to evaluate?

In the testing stage of the project, for every defined generalisation problem, the four generalisation systems were tested by 2 till 3 testers from the project team. All testers are familiar with generalisation but not necessarily with all the tested generalisation systems. Therefore we can distinguish between novices and experts of the systems. Moreover, the software suppliers were invited to produce outputs in parallel tests where they were allowed to design additional developments to their systems, in contrary to the regular testers who only used out-of-the-box versions of the generalisation systems. Consequently for some of the generalisation problems, there are also outputs available produced by improved and customised versions of the tested systems. Theoretically there could have been 16 outputs per generalisation problem (12 from regular testers and 4 from software suppliers). Because in practice not all the expected tests could be done, there are about 10 different generalised outputs per generalisation problem.

The objective of evaluating generalised data

Evaluating the generalised data produced with the tested generalisation systems aims at:

assessing the quality of generalisation output that current generalisation systems are able to provide knowing more about the domain of application (strengths/weaknesses) of the tested generalisation systems In other terms, we evaluate the generalised data in order to evaluate the systems, as described in [Ruas, 2001, p.15] as part of a "loop in evaluation between system and output". [START_REF] Mackaness | Evaluation in the map generalisation process[END_REF] distinguish between three types of evaluation: evaluation for tuning the generalisation system (prior to generalisation), evaluation for controlling the generalisation process (during generalisation), and evaluation for assessing the quality of generalised data (after generalisation). The evaluation task described in this paper falls into the last category, but is specific because (1) we use the results of the evaluation in order to get insights into the systems used to generalise the data, and (2) we will perform comparative evaluation on several outputs that are supposed to meet the same specifications to learn more about generalisation processes. Regarding evaluation for assessing, [START_REF] Mackaness | Evaluation in the map generalisation process[END_REF] distinguish three further subtypes: "evaluation for editing which aims to identify errors and mistakes (…).

descriptive evaluation which provides summary information on what has been removed, emphasised or altered, evaluation for grading, which seeks to derive an aggregated value reflecting the quality of the solution overall" [Ruas and Mackaness, 2007, p.105]. As we do not seek for ranking the tested systems, we will not concentrate on evaluation for grading. In order to learn more about the systems, we will rather perform descriptive evaluation as well as evaluation for editing (to detect well and badly generalised cases).

What do we evaluate?

In order to meet the evaluation objectives, an evaluation methodology was designed. Figure 1 presents a schematic view on the evaluation methodology of generalised data within the EuroSDR project. The ellipses presents the data related to one generalisation problem: one initial data set, the specifications describing the expected output data, and several outputs (1 to n), all of them intended to meet the specifications as best as possible. The two-directional arrows present the three main evaluation questions and show what data is involved to answer these questions. The three main evaluation questions are:

(1) How does each output respect the expected specifications (dashed arrows, right)? Here we consider one output at a time with respect to the specifications. This question will give insight into possibilities and limitations of commercial out-of-the-box software for automated generalisation with respect to NMA requirements. More precise questions are: are the provided solutions globally good? are the provided solutions good when looking in more detail to specific, local problems?

. . .

(2) How different are the outputs (curved arrows, left)? Here we consider all the outputs with respect to each other without regarding the specifications. We are interested if the provided solutions are very different as they are supposed to meet the same specifications. More precise questions are: are there cases that are handled very differently from one generalisation system to another and from one tester to another? (3) How differently do the outputs respect the specifications and, more importantly, why (straight continuous arrows, right)? Here we consider all the outputs with respect to each other and with respect to the specifications. More precise questions are: which generalisation systems are or are not able to handle which kind of problems? Are there cases handled in the same way by all the software? Are there cases that were not handled at all by any software? Is the quality of generalised data significantly better when the generalisation system has been enriched with dedicated developments? Are the results with a same generalisation system very different if the tester is novice or expert? etc.

Apart from these questions on characteristics of the generalised data, the evaluation will provide insight into the test protocol itself. For example how understandable and precise the specifications are that are expressed as a set of constraints by the four NMAs.

How do we evaluate the generalised data: three interconnected evaluation procedures

Three parallel but interconnected evaluation processes are set up to cover the list of what we evaluate presented in the previous section.

-An expert evaluation, where experts of the NMAs that provided the four tests assess the cartographic outputs. The conclusions of experts for different outputs are compared to get insight into the different quality of different outputs. The assessments performed by the experts will contribute to answer question (1) and the comparative analysis contributes to the answer of question (3). -An automated constraint-based evaluation, where the cartographic outputs are analysed to quantify the satisfaction of cartographic requirements by means of automatically computed constraint values and statistical indicators. These derived values are compared to give insight into differences in generalisation outputs. The computation of constraint values contributes to evaluation question (1) and their comparison across the outputs contributes to question (3). -An evaluation to compare generalised data, where the different cartographic outputs obtained for a given generalisation problem are directly compared, especially by investigating several specific local situations. This procedure enables to answer evaluation question (2) and contributes to evaluation question (3).

The methodologies for the expert evaluation, the automated constraint-based evaluation and the evaluation on comparing generalised data are described in section 3, section 4 respectively section 5, by addressing the specific objectives and related research, the methodology in more detail and the outcomes of the evaluation.

Expert evaluation

Objectives and related research

Quality assessment has always been an important aspect of map generalisation since derived data must satisfy various requirements in order to be a satisfactory generalisation solution: it should be fit for the desired map purpose, it should represent reality and it should be readable by the user after generalisation. Traditionally, generalisation results have been assessed visually meaning that domain experts have been evaluating whether the (manually) derived data sets meet the underlying requirements. Most researches on evaluating the quality of generalised data focused on evaluating the effect of generalisation on one object or on one feature class. Examples are [START_REF] Ehrliholzer | Quality assessment in generalisation: Integrating quantitative and qualitative methods[END_REF] and [START_REF] Bard | Quality Assessment of Cartographic Generalisation[END_REF]. Quality measures of interrelations of one feature class with other feature classes and of different requirements need further study. The objective of evaluating the generalised data by asking experts to assess it is related to these interrelations. More precisely the objective of the expert evaluation comprises 1) the assessment of the complete output maps and 2) the assessment of solutions for specific requirements. The key question in this evaluation is to what extent the outputs respect the requirements, where the requirements are considered to be laid down in the constraints. The global part of the expert evaluation will answer the question of what are we able to achieve with commercial software in automated generalisation addressing quality aspects at macro level. The second, detailed part of the expert evaluation has four objectives. Firstly, it will answer in what way current commercial software can handle specific constraints. Secondly the experts' assessments are compared to the output of the automated evaluation (see section 4). This comparison will yield insight into the (in)consistencies of both methodologies.

Thirdly the detailed evaluation will focus on the interaction of several constraints. Evaluating single constraints is not an absolute measurement for the quality of generalised data since it does not take into account that violating constraints might have been necessary in order to solve other more important constraint and also not that good outputs for constraints might be due to violating others, e.g. respecting minimal dimension between all buildings is possible because (too) many buildings were eliminated. To address the interaction of constraints, the expert evaluation will focus on specific locations of the map taking all involved constraints into account. A last objective of the detailed part of the expert evaluation is to compare the different assessment outputs in order to see how differently the generalised data respect the specifications. This will show which software is capable to handle which kind of problems (evaluation question 3).

The expert evaluation in more detail

To get insight into how experts assess the generalised data, a survey has been designed which extends an earlier experts' survey of the AGENT prototype [START_REF] Agent | Map generalisation by multi-agent technolgy[END_REF]. A first version of this survey was tested on outputs that were available in December 2007. Based on the first experiences, the survey was improved. Experts selected for the survey are experts that are familiar with the specific data in order to assure that they understand thoroughly what is expected in the outputs. For the global part of the survey the experts are asked to assess the whole map on the following aspects:

-Legibility -Level of manual editions required to meet the specifications -Deviation from initial data (ungeneralised data set) -Preservation of the geographic characteristics of the test area (urban, mountainous, rural or costal area) -Seriousness of main detected errors -Frequency of main detected errors -Number of occurrences of positive aspects -Information reduction (undergeneralisation / overgeneralisation) From the first version of the survey it could be concluded that it is impossible for experts to assess the solution for each constraint separately because these are too detailed. In addition there are constraints which cannot be assessed visually at all, such as orientation and position constraints. Consequently, for the improved version of the survey experts will be asked to assess the constraints on higher concepts. These are:

- The experts will be asked to assess these aspects for several feature types: buildings, roads, water, coastal features, relief (contour lines and spot heights) and land use. For the first version of the survey, the experts were asked to grade all aspects described above from 1-5 to be able to average grades for summarising the results.

For all objects

For one object For two objects For group of objects

Number of objects involved Number of criteria in each object category

Figure 2: Two outputs of expert evaluation for one data set. The graphs show the percentage of constraints for which solutions were graded as 1, 2, 3 or 4; divided between solutions for constraints on one, two and group of objects.

However, comparing the outputs of two different experts on the same output reveals the (expected) subjectivity of experts (see figure 2): the overall assessments are similar, but there are many differences when looking at the assessments in more detail. For example in the category "for all objects" expert 1 evaluated only two constraints with grade 4, while expert 2 rated between 9 and 20 constraints with grade 4 depending on the software system. In interviews afterwards, the experts of the first survey also indicated that the grades cannot be used as hard values; they depend on some obvious side effects, such as time of the day or whether the question was posed at the start of the survey, where the expert was still fit, or at the end. For the improved version it was therefore decided to only use nominal values: very bad, bad, good and very good. A medium category was deliberately excluded in the improved version of the survey in order to 'force' the experts to choose. The final part of the survey asks the experts to annotate the map with both good and bad examples, by specifically taking into account the interaction of several constraints (see figure 3 for an example output of the first version of the EuroSDR expert survey). The automated evaluation (see section 4) and the evaluation on comparing generalised data (section 5) will perform a pre-study so that experts will be pointed at situations with well or badly solved constraints are at situation that are of interest for a comparative analysis.

(A) The experience with the first version of the survey showed that care should be taken in assessing the solutions for constraints on preservation (shape preservation, relative position and inconsistencies between themes). At scale transition, these types of constraints are not violated assuming that the input data is correct. Good assessment by experts indicates that the initial situation is not deteriorated by the generalisation process. This might be either because the situation was not touched or because the system carefully took the preservation constraints into account.

In order to address evaluation question (3) a comparative analysis will be performed considering the outcomes for one test case. The assessment values for the four systems will be compared to see what software is appropriate to address which kind of problems and to identify cases handled in the same way by all the software and cases that were not handled by any software.

Expected outcomes

The assessments of the complete maps will result in a descriptive analysis addressing specific criteria. For the detailed part, the outputs of the expert evaluation will be summarized in tables per test case showing per constraint type, per software system, the assessment values of experts. Constraint types are classified based on the classification of [START_REF] Burghardt | Investigations on cartographic constraint formalisation[END_REF].

In addition further distinction is made between constraints on natural phenomena (expected to be more irregular) and manmade objects, as well as between polygons representing small objects (such as buildings), polygons representing land cover objects, points, and lines. Consequently the expert evaluation will be able to answer the question on what generalisation functionality is available and missing in current software detailing on all these aspects. It will also give insights into differences of the outputs produced by different systems. The annotated maps will provide a way to assess the interaction of several constraints.

Automated constraint-based evaluation: automatically comparing generalised data to what they are expected to be

Objectives and related research

To date, with the development of automated generalisation and the intention for modelling the overall process, it is remarkable that the automation of quality assessment still lacks in techniques. Beside a few studies, which only focus on sub-areas of quality assessment [START_REF] Brazile | Semantic Infrastructure and Methods to Support Quality Evaluation in Cartographic Generalization[END_REF]Skopeliti and Tsoulos, 2001;[START_REF] Cheung | Estimation of the Positional Uncertainty in Line Simplification in GIS[END_REF], [START_REF] Bard | Quality Assessment of Cartographic Generalisation[END_REF] was the first who contributed to development of a holistic assessment model proposing a general method to assess the quality of generalised map objects, as was already mentioned in section 3. An automated quality assessment system as part of an overall generalisation process has many advantages, not only for research, but also for map producers as National Mapping Agencies (NMA). Firstly, in contrast to visual assessment, an automated system allows to reduce both time and cost for the necessary quality control within a production process. Nevertheless setting up such a system and preparing the data for the evaluation takes time, thus within the EuroSDR project even more time had to be invested on the automated evaluation than on the expert evaluation. Secondly, automated evaluation may support the improvement of generalisation processing with the identification of commonly occurring cartographic errors, the provision of further meta-data (e.g. descriptive information about modifications resulting from generalisation), the integrated comparison between alternate generalisation solutions and the improvement in specifying requirements to generalisation.

Automated evaluation of generalisation results can be performed on the basis of cartographic constraints. Crucial for that is the degree of constraint formalisation, which should give as much as possible implementation details. A cartographic requirement such as "initial and generalised shape should be similar" can be evaluated through very different shape measures and is less formal than the requirement on "preserving the width-length ratio" for example on a building shape.

On the basis of specified cartographic constraints provided by the NMAs, a typology of constraints was derived which reflect the main requirements to generalised data (see [START_REF] Burghardt | Investigations on cartographic constraint formalisation[END_REF]). As a result, sets of constraints build the basis for the quality assessment of generalised test data sets.

The automated constraint-based evaluation pursues following purposes within the EuroSDR project:

a. Identification of constraint violations reflecting cartographic conflict situations. b. Derivation of aggregated and average values representing the quality of a part or the overall generalisation result. c. Provision of summary information on modifications resulted from generalisation (e.g. statistical analysis)

Both, purpose a) and b) are applied against specific cartographic constraints. Figure 4 illustrates the interaction of the components within the automated evaluation framework.

A set of cartographic constraints for a specific data set is principally derived from two main requirements to generalisation, namely the improvement of legibility and the preservation of appearance. In case of legibility, constraints are specified independently from initial data set, that is, they are defined by thresholds. On the contrary, constraints, which aim at preserving the appearance, are defined subject to the initial data set (reference data set) which is assumed to be correct. Legibility constraints as well as preservation of appearance constraints relate to specific and measurable map object properties as for instance size, position or orientation. On the basis of the measured property values in the initial (ungeneralised) and final (generalised) state, the actual evaluation procedure can be applied for preservation constraints. The evaluation contains the comparison of the measured final value with a calculated or predefined ideal final value as explained at the end of section 4. The difference results in a constraint violation between 0 and 1 whereas a maximal violation is equated with a constraint violation = 1. The resulting degree of constraint violations can then be qualitatively interpreted for grading the whole generalisation result or local solutions. Workflow. After loading of the ungeneralised and generalised data, the object properties such as symbol width and id's has to be selected necessary for the evaluation process. This is an important step since the entity relationship model (vertical relations such as 1:1-, 1:n-and n:m-relations) as well as the extent of symbolisation (e.g. width of line) are incorporated in the evaluation process in this way. The latter is necessary because the symbolisation information can not be exchanged directly by the typical vector exchange formats. Further the cartographic constraint will be choosen for the automated evaluation with the corresponding parameter setting. The system lets the user set these parameters but in order to minimize the time exposure in case of repetition, the schema can be stored. Finally the evaluation process is carried out with the calculation of constraint violation values, which are listed in the output window with further statistical information. Due to the huge number of evaluation results and in order to enable a purposeful control of specific map objects, a visualisation tool is implemented.

Expected outcomes on the example of legibility constraints

In contrary to preservation of appearance constraints, legibility constraints are simpler to evaluate since they are only evaluated against a predefined and explicit threshold. There is no dependency on reference data (initial data set). However, the interpretation of constraint violation raises a problem. The constraint violation principally ranges from 0 to 1, whereas 0 indicates no constraint violation and 1 is maximum violation. In case of legibility, a threshold defines the minimum allowed size of part of an object or of the object itself, as well as the minimum allowed distance between objects, that is, the corresponding final value must respect these legibility thresholds. Consequently, there are two possibilities: firstly, the final value exceeds the threshold, or it is under the threshold (Boolean case). The usage of two concrete cases is advantageous for the identification of cartographic errors, but the question arises whether a Boolean approach is meaningful for grading generalisation solutions since information about the distance between the final and ideal final value is not incorporated.

With an example of evaluating three legibility constraints, we want to illustrate the expected outcomes: The examples provided here focus on building and road objects in rural areas. The data (topographic map) used in this example is the generalisation problem of TD Kadaster (Netherlands) where the final data set at scale 1:50'000 is derived from data at 1:10'000.

a.
The evaluation results for the three constraint examples show the degree of constraint violation on all map objects, that is, the corresponding mean constraint violations state whether the underlying constraints are globally satisfied or not. A further value indicates the mean deviation from the threshold in case of constraint violations. In support of the mean deviation value, the prototype provides additional information as for instance statistical dispersion.

As mentioned above the constraint-based evaluation of preservation of appearance constraints is much more complicated since the ideal final value can rarely be defined explicitly. This problem holds especially true for shape constraints. In order to be able to determine an ideal final value, that is, the quantitative description of target shape, various aspects must be previously considered. Firstly, it is hardly impossible to describe mathematically the general shape of a map object.

There are a huge number of existing measures for the quantitative description of shape specific properties such as concavity, elongation or shape index. At this point, the question arises: which existing shape measures are suited for an optimal shape description and evaluation? If this question is answered, shape of map objects can be measured meaningful to quality assessment.

A second problem, as mentioned at the beginning, is the calculation of the ideal final value of a map object's shape: which are the modifications to shape in generalisation at a certain scale transformation? Hence, scale depending modifications to shape must be formalized in order to be able to determine ideal final values for any starting situations. The extraction of this information can be carried out among others by means of reverse engineering, that is, gathering knowledge by studying existing maps on different scales [Harry, 2001]. It seeks to establish which transformations are needed to describe an optimal (or correct) solution on a specific scale. The technique we will apply for the information extraction concerning shape of individual map objects is statistical analysis of manual generalised maps of high quality. The results of this analysis are regression functions which describe the relation between independent variable (initial value) and dependent variables (final value).

5 Comparative evaluation: comparing several generalised datasets between each others

Objectives

As presented in section 2 of the paper, for each generalisation problem there are up to 16 different cartographic outputs, all supposed to meet the same specifications. The comparative evaluation procedure compares these cartographic data in order to identify and hopefully explain the kinds of differences between them as suggested by [Ruas, 2001, p.19]. The objectives of this comparative analysis performed directly on the generalised data are:

1. To assess if the obtained generalised datasets are very different from each other. without considering the specifications, i.e. we do not try to assess if some outputs are better than others. We just try to have a rough idea, from a quantitative point of view, of how different the generalisation outputs can be given a set of specifications.

2. To describe more in depth, from a qualitative point of view, the differences noticed between the different generalised datasets. Here the specifications are taken into account: we analyse how differently the generalised datasets respect the specifications. And we try to outline possible correlations between the way a given problem is handled and the used generalisation system, the tester, etc.

The methodologies proposed for achieving these two objectives including the expected outcomes, are respectively described in sections 5.2 and 5.3.

Measuring how much the generalised datasets are different

Figure 6 shows that outputs obtained from the same dataset can be very different. Even though the symbolisation differs from one extract to another one, it is obvious that far less buildings have been kept in extract 3. In order to measure how different the outputs are, we will perform some quantitative analysis as proposed by [START_REF] Mackaness | Evaluation in the map generalisation process[END_REF], and analyse the distribution of values across the set of cartographic outputs related to the same generalisation problem. This analysis concerns total numbers for small objects like buildings, cumulative lengths for networks, and cumulated areas for land cover. They will be computed at the macro level i.e. regarding "all buildings", "all roads", "all rivers", etc. If the outputs visually appear to be very different in specific areas, the analysis will also be performed on parts of the datasets like meshes of the road network (meso level as defined in [START_REF] Ruas | The Roles Of Meso Objects for Generalisation[END_REF]).

On top of these computed indicators, interactive visual comparison will be used to notice general trends. As mentioned above, this interactive visual comparison will also enable to know where to compute indicators in order to confirm, by means of numbers, trends that have been visually detected.

Qualitative analysis of the differences between the generalised datasets

Besides quantifying how much the outputs obtained from one dataset are different, we also wish to qualitatively analyse how they differ and, when possible, to explain the encountered differences. If two outputs are locally different (spatially or thematically), several cases can be distinguished:

-The two outputs respect the expected specifications (the constraints): it means the specifications are flexible enough to enable very different interpretations. It is known that generally, several solutions exist to one generalisation problem [START_REF] Spiess | The need for generalization in a GIS environment[END_REF]. If these solutions are very different, probably the specifications are not precise enough. -At least one of the outputs does not respect the specifications: three main situations can occur regarding the case that does not respect the specifications: (1) the concerned tester has succeeded in translating the specifications into the system, but there is a bug in the system linked (2) the concerned tester did not succeed in translating the specifications into the system, because the system (2a) or its documentation (2b) is incomplete or (2c) the specifications were not understood by the tester, (3) the concerned tester thinks he has succeeded in translating the specifications, but actually he misinterpreted one part of them, possibly because they are not clear enough.

It should be possible to identify the situation (2) by analysing the "constraints expression template", a template filled in by every tester for every dataset and system, where the tester mentions for each constraint if he has succeeded in expressing the constraint in the system and, if not, why not. Comparing the constraints expression templates (and the outputs) of novice and an expert testers for the same system will help to discriminate between situations (2a) and (2b). Looking at the results obtained by the software suppliers in their parallel testings could also help.

In the same way, the situation (3) can be identified if the results obtained by the same system are very different whereas their constraint expression templates look the same. Situation (1) would rather correspond to the same problem appearing in all the outputs produced with a given system.

The comparative analysis of the outputs will mainly be based on careful visual investigations based on the following elements of methodology:

-Zones of particular interest will be defined for each dataset, on which the comparative analysis will focus. -Figure 6 shows the importance of having similar symbology. Therefore the symbolisation of all outputs has been redone by one person. -A grid to help further analyses will be filled in during the visual investigation stage: The expected outcomes of this visual comparison are: for every "zone of interest" of every generalisation problem, a series of small extracts from the different cartographic outputs, annotated with a qualitative description of how they differ and an attempt of explanation of why they differ. If the counting's performed as presented in section 5.2 show significant differences from one dataset to the others on a particular aspect, we will also try to explain these differences at this stage.

How automated constraint-based evaluation, expert evaluation and comparative evaluation support each others

In the expert evaluation cartographers will assess both globally and in detail to what extent the outputs meet the requirements and if differences can be identified between systems (what system is best capable of handling which problem). The automated constraint-based evaluation provides values for the satisfaction of specific constraints (mainly legibility constraints) for every test data set for every of the four software systems and will study if quality differences can be identified between the four systems. The comparative evaluation will investigate common patterns and heterogeneity between the different output data for one test case achieved by different testers with the four generalisation systems. This last evaluation will yields insight into 1) different solutions for one generalisation problem and 2) which system is better suited for the generalisation of a specific test data set or group of constraints. Synergy between the three evaluation procedures is accomplished because findings will be exchanged and examined to answer the three main research questions raised in section 2.3.

Example of interaction and interchange are:

• the quality results for specific constraints produced by the automated evaluation will be compared to the quality results for the same constraints evaluated by experts;

• the systematic automated and expert evaluation for specific constraints and situations will identify aspects on which a closer interactive analysis is needed in the comparative analysis (for example situations where the quality of the solutions differ considerably); • a first run of the comparative evaluation and the automated constraint-based evaluation will identify situations to which experts will be pointed in the expert evaluation in their task to annotate the output maps with remarkable solutions (very good, very bad or very differently solved solutions); • the automated and expert evaluations on particular constraints can confirm or invalidate trends noticed through the visual comparative analysis; • the results achieved in automated constraint-based evaluation by standard deviation analysis will be compared to the visual comparative analysis. Besides these integrations, a comparison between the outputs of the three evaluation processes will be done to detect inconsistencies between the three types of evaluations that need to be further investigated (e.g. bugs or inappropriate measuring tools in the automated evaluation process; misinterpretation of what was asked to the experts; subjectivity of the evaluations).

Conclusion and perspectives

This paper has presented the three main questions that are the focus of the evaluation of generalised data within the EuroSDR project. How does each output respect the constraint specifications? How different are the outputs? How differently do the outputs respect the specifications and what are the reasons for that? The evaluation is supported by several evaluation processes for which the methodologies were presented here, including some initial experiences. After an extended evaluation phase (summer 2008), it will be investigated how much the results of the three evaluation processes can be integrated further to answer the three main evaluation questions of the project. Based on these first results, the last part of the evaluation will be carried out in autumn with fine-tuned methodologies.

Figure 1 :

 1 Figure 1: What do we evaluate for one generalisation problem ?

Figure 3 :

 3 Figure 3: Annotated map with good and bad examples (A) and explanation of examples (B)

Figure 4 :

 4 Figure 4: Framework of automated constraint-based evaluation

Figure 5 :

 5 Figure 5: Prototype for automated constraint-based evaluation

Figure 6 :

 6 Figure 6: Extracts of outputs obtained from the IGN-France dataset by three different testers using different systems.

  Constraint 1: target width of protrusion {buildings} ≥ 0.25 map mm

	Number of map objects	Number of map objects	Mean constraint	Mean deviation from
	without constraint	with constraint	violation	threshold in case of
	violation CV = 0	violation CV = 1		CV = 1 (in map mm)
	521	27	0.0493	0.08
	b. Constraint 2: target area {buildings} ≥ 0.1225 map mm 2
	Number of map objects	Number of map objects	Mean constraint	Mean deviation from
	without constraint	with constraint	violation	threshold in case of
	violation CV = 0	violation CV = 1		CV = 1 (in map mm)
	539	9	0.0164	0.021
	c. Constraint 3: target distance {buildings, roads} ≥ 0.25 map mm
	Number of map objects	Number of map objects	Mean constraint	Mean deviation from
	without constraint	with constraint	violation	threshold in case of
	violation CV = 0	violation CV = 1		CV = 1 (in map mm)
	527	21	0.0383	0.11

Acknowledgments

The authors would like to thank all participants of the EuroSDR project, in particular we wish to thank Maria Pla (ICC, Catalonia), Peter Rosenstand (KMS, Denmark), Karl-Heinrich Anders (University of Hannover, Germany), Annemarie Dortland, Maarten Storm and Harry Uitermark (TD Kadaster, The Netherlands), Magali Valdeperez, Francisco Martínez and Francisco Dávila (IGN Spain) for the discussions and contributions during the project meetings.


Gabin Mantulet

Adrien Bidaud

Silvana Mima

The role of

Keywords: Long-term energy modelling, decarbonisation, bioenergy, gasification, methanisation, biogas, CCS, flexibility

come

Introduction

Climate change is a major challenge to be urgently tackled nowadays and we aim at reducing human induced greenhouse gases (GHG) emissions because of global warming. The energy sector represents almost 80% of GHG releases, so that renewables and sustainable energy vectors have to develop massively to replace fossil fuels [START_REF] Mima | Global warming of 1,5°C[END_REF]. As well as known renewable energy as solar, wind or hydro, bioenergy represents a real feedstock for GHG emissions reductions. REmap 2030 from the International Renewable Energy Agency [START_REF]REmap 2030 a renewable energy roadmap[END_REF] shows the importance of bioenergy for meeting international targets for renewable share and for energy system decarbonisation, in particular bioenergy consumption should increase by 60% from 2015 to 2030 with climate policies implementation. Furthermore, bioenergy would play a major role in two of the three pillars in all deep decarbonization project scenarios [START_REF]Pathways to deep decarbonization 2015 report[END_REF] where bioenergy will reduce the carbon content of all transformed energies and will switch energy end-uses to lower-carbon, and even zerocarbon, energy carriers. While other renewables are only used to make green electricity, bioenergy can be used in lots of energy sectors: for electricity but also for liquid fuels, biogas, and hydrogen production. This flexibility makes bioenergy and bioenergy technologies valuable for the decarbonisation of energy use [START_REF] Mima | Global warming of 1,5°C[END_REF].

The official French Energy legal code specifies that: "Biomass is the biodegradable fraction of products, wastes and residues from agriculture, including plant and animal substances from the land and sea, forestry and related industries, as well as the biodegradable fraction of industrial and household waste" [START_REF]Code de l'énergie français, Articles D211-1 à D211-4[END_REF]. Bioenergy therefore means energy content in solid, liquid and gaseous products derived from biomass feedstocks including solid biomass, biofuels and biogas. Bioenergy is also a way to reach political objectives. For instance in the European Union, renewables have to represent 20% of the final energy consumption by 2020 (23% in France) and 27% by 2030 (32% in France) [START_REF]Annual European Union greenhouse gas inventory 1990-2015 and inventory report 2017[END_REF]. Indeed, biomass is one of renewable and low carbon feedstocks that represents many sources of energy that are storable and compatible with many valorization processes. Its energetic valorization in electricity, heat, biogas or biofuel is compatible for different energy services, in which networks can be interconnected. Finally, yet importantly, considering the entire life cycle, bioenergy coupled with some carbon capture and storage (CCS) devices can bring negative carbon emission for energy systems, that is an efficient way to reach Paris climate accord' objectives [START_REF] Iea | Energy Technology Perspectives 2017: Catalysing Energy Technology Transformations[END_REF].

Two different aspects come to the fore. The first one is the relevance of bioenergy valorization systems in energy supply compared to other sources of energy such as fossil fuels or renewables energies: what will be the share of bioenergy use in next decades? The second one is the energy technologies and conversion processes prioritization for biomass utilization: what will be the routes favoured and those neglected? Indeed, the diversity of biomass exploitation leads to competition among bioenergy value chains that depend on local parameters such as countries' biomass potential, economic growth, policies strategies, technologies maturities, etc. [START_REF] Iea | Energy Technology Perspectives 2017: Catalysing Energy Technology Transformations[END_REF].

Only few research papers [START_REF]Biomass resources and their bioenergy potential estimation: A review[END_REF][START_REF]Life cycle assessment of pyrolysis, gasification and incineration waste-to-energy technologies: Theoretical analysis and case study of commercial plants[END_REF][START_REF]The water footprint of second-generation bioenergy: A comparison of biomass feedstocks and conversion techniques[END_REF][START_REF] Liu T | Biogas system in rural China: upgrading from decentralized to centralized?[END_REF] or thesis works [START_REF] Pozzobon V | Biomass gasification under high solar heat flux[END_REF][START_REF] Horton S-R | Modelling municipal solid waste gasification: molecular-level kinetics and software tools[END_REF][START_REF] Boiler | Energy system evaluation of thermo-chemical biofuel production: Process development by integration of power cycles and sustainable electricity[END_REF] address the role of pyrolysis, gasification, methanisation as independent decarbonisation options in prospective energy-modelling tools. This work will carry out a more detailed analysis on the local availability of biomass resource, the local bioenergy technologies appropriation and their role for achieving climate goals. The paper is structured as follows. Section two presents the main features of bioenergy and valorization technologies. Then we describe in section three the modelling in POLES and the methodology used. In section four, we present the analysis and local trends in bioenergy use. Section five summarizes the study and discusses further implications.

Biomass as a source of decarbonised energy and related gas valorization technologies

The energy source tackled in this paper, biomass, is closely linked with gas energy carrier. Indeed, section 2.2. presents the technologies that convert biomass in bioenergy that produce gas. Gas, as an energy carrier, has two key strong point in order to reach global emissions reductions that are required to keep the rise in global average temperatures down to 2°C and to improve the world's air quality. First, versatility: gas can play multiple roles across the energy system in a way that no other fuel or technology can match. Gas can generate electric power, provide heat, and be used for mobility. Second, the environmental dimension: combustion of gas does produce some nitrogen oxides (NOX), but emissions of the other major sources of poor air quality, particulate matter and sulfur dioxide (SOX), are very tiny. The combustion of natural gas releases also approximately 20% less CO2 than oil, 40% less than coal by unit of energy and even well under half of what a coal plant emits with combined-cycle gas turbines by unit of electricity produced. As gas infrastructures and networks are well developed worldwide, this energy carrier would play a major role in energetic transition thanks to its storage capacity. Last but not least, gas-fired power plants also have technical and economic characteristics that are complementary with a strategy favouring the expansion of variable renewables with playing a lower carbon back-up role than coal or oil [START_REF]Renewable Energy Innovation Policy: Success Criteria and Strategies[END_REF]. We will describe in this chapter the several type of biomass resource and the technologies, methanisation and gasification, that transform biomass into biogas.

Biomass feedstock and conversion routes description

Biomass feedstock is separated in three main categories [START_REF] Dupont C | The main biomass to energy routes[END_REF]. First category gathers food production relative cultures: sugarcane, beetroot, corns, colza, maize, sunflower, palm or even soya. These resources aim at feeding the planet but are also convertible in biofuels as biodiesel for oil seeds and bioethanol for sugar seeds. Direct combustion and biofuel production are is their possibility of energetic valorization. Second category is referring to lignocellulosic resource: forests, plants for energetic purpose such as Miscanthus and agricultural wastes. Several energetic valorizations are possible: direct use as heat valorization, biofuels (Diesel Fisher-Tropsch and Ethanol) and electricity or gas conversion. This category of biomass will be the centre of the paper because of its high potential and advanced end-uses possibilities. Last feedstock is composed of algae and microalgae. For the moment, these conversion processes are only at an early step of R&D. Their maturities are still limited compared to other generations, so that we will not discuss this resource in this paper.

All types of biomass use the CO2 in the air, in the soils, in the water in order to grow through photosynthesis process. Because this resource represents a way to store carbon, the main strength of biomass is the carbon neutrality. Even if there are some discussions about the real carbon content of the entire life cycle of biomass, it is common to assess that the carbon released during energetic valorization of biomass has been stored during its growth, so that the carbon footprint is lifecycle neutral or even negative with Carbon Capture and Storage (CCS) [START_REF] Dupont C | The main biomass to energy routes[END_REF].

Figure 1: POLES bioenergy pathways

We describe briefly the main features for the two main energetic valorization paths for second-generation biomass that are considered in this paper: methanisation and gasification.

Technologies consideration 2.2.1. Methanisation

Methanisation, also called anaerobic digestion, is a biochemical process of biomass valorization into a gas mixture called biogas. Table 1 sums up the different methanisors design and the costs associated [START_REF]ATEE Association technique énergie environnement, Club biogaz[END_REF]. [START_REF] Grdf | le Syndicat des énergies renouvelables (SER), le SPEGNN et Teréga[END_REF].

Nowadays, the major valorization after the biomass methanisation is a cogeneration with electricity and heat production from the biogas. However, this biogas can also be upgraded in biomethane after a methanation process in order to have a gas that can be injected in gas network, with an 80% lower carbon content than fossil gas [START_REF]Club Pyrogazéification -Filière ''Injection de biométhane de synthèse[END_REF].

Gasification

Gasification is a thermochemical conversion route for biomass valorization into a gas mixture called syngas [START_REF] Dupont C | The main biomass to energy routes[END_REF]. Table 2 presents the different types of gasifiers with their features. Biomass gasification plants are still in a demonstration point. Indeed, even if the energetic conversion yields are important, from 60% to 90%1 [START_REF]Club Pyrogazéification -Filière ''Injection de biométhane de synthèse[END_REF], some progress has to be done on the costs in order to be competitive: nowadays, costs raise approximately to 100 000€ per kW installed that is at least ten times more than methanisors [START_REF] Holmgren K | Investment cost estimates for biomass gasification-based systems[END_REF]. Europe and mainly Germany, the Netherlands and Northern countries are leaders with several demonstrators in progress such as Gobigas (20MW-installed power since 2014), bioSNG in Güssing, or Lahti Energy's Kymijärvi II [START_REF]European Technology and Innovation Platform Bioenergy[END_REF][START_REF] Gaya | [END_REF][START_REF]Project Lahti Energy's Kymijärvi II[END_REF]. Other countries are still in a RD&D phase with demonstrators in construction or exploitation such as in France with Gaya or Futurol, etc. [START_REF]Project Gobigas[END_REF][START_REF]Project bio SNG in Güssing[END_REF]. There are also some projects in the USA, mainly to produce biofuel [START_REF]Project sundrop fuels[END_REF][START_REF]Project fulcrum[END_REF], but also in China, India, Brazil or Canada [START_REF] Dupont C | The main biomass to energy routes[END_REF].

Complementarities

The route of injection of synthetic biomethane from the gasification sector is aimed at the conversion of lignocellulosic dry carbon materials such as forest or agricultural residues as well as renewable recycled materials, by-products of waste treatment (sludge). It is generally not suitable for the recovery of materials with very high moisture content that are rather intended for the biomethane production by anaerobic digestion. These two processes are therefore complementary since they mainly use different inputs. In addition, it should be noted that methanisation could also use methanation as a downstream process, to recycle the CO2 contained in the biogas. It allows the production of a synthetic biomethane with a "renewable" carbon dioxide origin that can be added to gasification process.

Methodology

Our study aims at analyzing the availability of biomass resource and biomass valorization technologies gasification and methanisation and their role in the decarbonisation strategies using a model based approach. The model used is the Prospective Outlook on Long-term Energy Systems (POLES), developed by Enerdata, the European Commission Research Center and the CNRS, an academic research institute in France. POLES has a strong background in providing analyses in the area of climate change and energy. It was already used in many European studies such as the GECO reports from European Commission Joint Research Center [START_REF] Keramidas K | Global Energy and Climate Outlook 2018: Sectoral mitigation options toward a low-emissions economy[END_REF], for Energy Modelling Forum studies [START_REF] Bauer | Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison[END_REF] as well as for IPCC reports [START_REF]Fifth Assessment Report (AR5)[END_REF]. Actually, we can consider POLES as validated thanks to these previous recognized studies using this model and the constant effort to update POLES structure and data [START_REF] Despres | POLES-JRC model documentation[END_REF].

The POLES bioenergy modelling

The POLES model is a bottom-up long-term energy-modelling tool, seen as a partial equilibrium model. Bottom-up means that the energy demand is represented at a detailed level (equipment, uses, technologies, markets, etc.). It is a large-scale system where the world is divided in 66 regions. It aims at exploring long-term developments in energy systems until 2100 with a year-by-year simulation of supply, demand and price interactions. The modelling applies simulations with full interaction of demand and supply dynamics with price changes through lagged adjustments. POLES helps us to generate diversified energy scenarios and in particular to take full account of GHG abatement policies in a consistent economic framework (i.e. through carbon tax, emission quotas or regulation).

The model takes as input parameters: the projected population, Growth Domestic Product (GDP) by region, oil and gas resources, technology costs and performances. Then it provides as outputs the international and national energy prices, full IEA-type national energy balance with 13 final sectors, diffusion of about 30 key energy technologies. It details the energy model at world level with economic consistency provided by the central role of prices and technological change.

The biomass sub-architecture is detailed as follow. Each feedstock is represented and the potential assessed for each region is associated to a dedicated surface for culture, a harvesting yield, and a marginal cost curve. The combination of these parameters gives us the technical and economic potential for each feedstock for each region. Resource allocation is determined endogenously with a merit order algorithm where end-use needs are answered by minimizing the system cost where algorithm considers feedstock, trade, other resources' competition to fulfil requirements. It is worth noticing that biomass is allocated for energetic purpose without going into competition with food requirements and land sustainability. Therefore, biomass allocation will depend on its local yields and availability, on other resource prices, on the overall demand, on availability and cost of transformation processes (CAPEX and OPEX) and the expected R&D progress. It is worth noticing that all these features depend on the region, the time and the scenario considered [START_REF] Despres | POLES-JRC model documentation[END_REF][START_REF] Lepii | POLES 5 -PROSPECTIVE Outlook on long-term energy systems[END_REF].

Scenarios implemented

We carry out some energy scenarios that span across XXI st century. These scenarios are defined according to different political energy strategies that drive to different energy uses and energy mixes and their relative carbon emissions.

The scenarios study the sensitivity to several parameters. First, it's worth noticing that world population, growth domestic products, coal, oil and gas reserves and biomass potential come from International Energy Agency (IEA) [START_REF] Iea | World Energy Outlook 2018[END_REF] and GLOBIOM databases [START_REF] Valin H | Description of the GLOBIOM (IIASA) model and comparison with the MIRAGE-BioF (IFPRI) model[END_REF], so are taken into account exogenously in our scenarios. We propose two main scenarios: a baseline one that is the extension of actual energy policies, and the 2°C scenario that translates the application of climate policies aiming at limiting global warming under 2°C in 2100, which is more stringent for CO2 releases. In this last scenario, a carbon tax is introduced so that prices are higher for energy vectors that release CO2 such as coal, oil or gas and their derivatives: gasoline, fuels, coke, etc. Hence, unlike fossil fuels, cleaner technologies that do not release GHG are fostered and their development speeded up. The goal is to respect the associated carbon budget 900GtCO2 eq releases until the end of the century for 2°C warming limitation.

Then a major sensitivity analysis is done. 2°C scenario is divided in two declinations, one with CCS technology and the other without. Indeed, and we will see it in the results presented section 4., the future of CCS deployment is the key parameter developing widely or not bioenergy or gas plants for decarbonisation, and more precisely lignocellulosic gasification: "All pathways that limit global warming to 1.5C with limited or no overshoot project the use of Carbon Dioxide Removal (CDR) on the order of 100-1,000GtCO2 over the XXI st century" [START_REF] Mima | Global warming of 1,5°C[END_REF]. However, doubts remain about the economical profitability of CCS deployment because of concerns about CO2 storage sites feasibility, technologies costs and real potential of implementation [2] [34]. Moreover, low fossil fuels prices, current relative failure of CO2 quotas markets, local legislation, etc. do not send incentives for this field development [START_REF] Iea | Energy Technology Perspectives 2017: Catalysing Energy Technology Transformations[END_REF].

Scenarios spread until 2100 where long-term effects are depicted. A strength of the study is the separation of the biomass valorization technologies in this long horizon time, so that we are able to determine the penetration rate of methanisation and biomass gasification. The strong point of the model is the endogenously consideration of bioenergy allocation paths, from potential to technologies so that results are well integrated. That is directly linked with demand and needs, end use competition for biomass resource and technological competition.

Outputs are declined for several perimeters: the world and some regional trends. We take into account several representative countries that drives bioenergy markets worldwide: the European Union, China and the USA as dynamic continent country with different growth rates and Brazil as a Southern country and as the leader of world biofuels with the largest biomass potential in the world. The potential of biomass used, the trade of the resource, the importance of each final use (electricity, heat, gas, biofuel) and the development of each technology tackled (gasification and methanisation) for the different time and land scales covered will be presented. We put all these results in comparison with the GHG emissions representing the climate paths chosen. We assume that GHG emission variations follow CO2 emissions variation that is a hypothesis already made by IEA [START_REF] Iea | World Energy Outlook 2018[END_REF] and Eurostat's [START_REF]Annual European Union greenhouse gas inventory 1990-2015 and inventory report 2017[END_REF].

Scenario comparison

At the beginning, we will compare results from POLES to World Energy Outlook (WEO) of IEA [START_REF] Iea | World Energy Outlook 2018[END_REF] and World Energy Council (WEC) scenarios [START_REF]Les Scénarios Mondiaux de l'Énergie à l'horizon 2050 -Mise en musique des futurs de l'énergie[END_REF] for the energy and bioenergy trends in the first part of the XXI st century in order to have coherent order of magnitudes. The differences with our modelling and our study is the time scaling: our scenarios span across the entire XXI st century until 2100 whereas others models look at the future until 2040 or 2050.

Moreover, POLES has a deeper technological separation so that is useful to analyse precisely the technological development for bioenergy expansion. Last but not least, the regionalisation of POLES allows us to study and compare several regions of the world and the main trends in bioenergy development depending on land specificities.

4. Results

A growth of bioenergy potential and consumption in the XXI st century

First, it is worth noticing that biomass potential is assumed independent of the climate scenario chosen. In reality, it is not entirely true because of different global warming in scenario that imply different effects on land available, yield that change the production: "[Global warming induces] highly penalized productions in the climates of low-latitude countries, whereas those of temperate climates could be slightly favoured, at least as long as the warming remains below 2°C or 3°C. But beyond that, warming should also result in a drop in yields" [START_REF] Keramidas K | Global Energy and Climate Outlook 2018: Sectoral mitigation options toward a low-emissions economy[END_REF]. Nevertheless, it is a correct assumption in order to compare the biomass consumption in regard of a global maximum potential. The biomass potential data come from the Global Biosphere Management Model (GLOBIOM) [START_REF] Valin H | Description of the GLOBIOM (IIASA) model and comparison with the MIRAGE-BioF (IFPRI) model[END_REF] developed by the International Institute for Applied Systems Analysis (IIASA), and provides for each year from 2000 to 2100 the yield for each biomass feedstock and crop, the land available for food and energetic cultures, etc.

Global trends and other models comparison: a consensus of increasing biomass consumption in climate policies scenarios

Overall biomass consumption triple from 2015 to 2050 and quintuple from 2015 to 2100. A higher share of the potential is used from 23% in 2015 to the range 75%-85% according to the scenario considered in 2100. Two main trends exist: the first generation consumption for energetic purpose decreases in the XXI st century while second generation biomass consumption explodes. The first generation consumption reduction results from an increasing need for food production because of world demographic growth expected from 7.5 billion people today to 9.5 billion in 2050 and 10.5 billion in 2100. Hence, this competition decreases the potential and the controversial utilization of first generation biomass for energetic purpose. In the same time, we observe a linear increase in second-generation biomass consumption with a growth of the potential for short rotation crops for energetic purpose and a stable wood resource potential. Bioenergy contributes to climate change mitigation by replacing some fossil fuels uses in electricity, transportation or heat valorization by local resource [START_REF] Thran D | Milestones 2030, elements and milestones for the development of a stable and sustainable bioenergy strategy[END_REF]. In addition, biomass consumption is higher in 2°C scenario than in baseline because of CO2 emission limitation goals and so increasing need of fossil fuel substitution [START_REF] Guo M | Bioenergy and biofuels: history, status and perspective[END_REF].

The three studies assess primary energy consumption and energy consumption from biomass in 2040 2 and can be compared with 2015 (figure 2). Even if there are some differences, where POLES modelling gives a little bit more importance to bioenergy than other models, the order of magnitude is coherent for all scenarios conducted. While bioenergy consumption increases for all scenarios between 2015 and 2040, this growth remains limited for baseline. Coupled with the high increase in total energy consumption (around 40% from 2015 to 2040), the share of bioenergy in energy mix moves back a bit for AIE and WEC and remains constant in POLES. Hence, bioenergy is just following the global consumption growth trend but does not play a substitution role in energy mixes that still relies on majority (about 75%) on fossil fuels: oil, coal and gas. The trend is the opposite for climate policies scenarios. Although overall energy consumption increases when compared to today, the growth is 80% lower in climate policies scenarios compared to baseline due to stronger sobriety and efficiency policies applications. In the same time, bioenergy is higher in all models. Therefore, the share of biomass in the energy mix is more important in 2040 than in 2015 for the three models: there is almost the double share for biomass use in climate policies scenarios for each valorization compared to baseline. For example, bioenergy resource increases in share in electricity represents 2.1% of energy produced in 2015 to around 6.8% in 2040 in POLES and around 5% for the others models. These figures highlight the substitution role played by biomass where fossil fuels are not anymore in majority in world energy mix for electricity, heat and fuels production and are partially replaced with bioenergy.

Figure 2: primary energy and biomass consumption -WEC, IEA and POLES models comparison

This allows us to validate our model compare to other well know economic models thanks to comparable outputs and future trajectories for each scenario. As seen in the IEA, WEC and POLES scenarios, bioenergies will play an expanded role in the energy mix in all scenarios with climate policies and a decreasing role in baseline scenarios with a massive use of fossil fuels, notably coal, in developing countries, which will represent the majority of the energy consumption growth. Let us consider now the several bioenergy uses and its global repartition.

Different uses patterns depending on scenarios considered

Heat is currently the main valorization for biomass because of a massive use of traditional combustion in the world. However, for 2050 and 2100, a growth in quantity and share for advanced valorization, electricity and secondgeneration biofuel, is highly expected. In the same time, traditional heat valorization is replaced by advanced heat valorization with higher performance index and less gas releases (figure 3). For instance, the efficiency of wood heating is very dependent on the technology used: efficiencies come from 10% for open fires to more than 90% for automatic boilers with platelets or pellets [START_REF] Ademe | Le chauffage au bois[END_REF].

Figure 3: biomass potential and end-use consumptionworld

As mentioned previously, bioenergy consumption is higher in 2°C scenario. For a same potential and feedstock, a different repartition of resource valorization is observed. Indeed, a larger quantity of electricity is produced with biomass in 2°C scenario where climate policies and emissions reduction goals speed up the end use electrification, the replacement of electric generation from coal, oil and natural gas and reduce the overall fossil fuel consumption for transportation. Besides the possibility to add carbon capture and storage facilities with electricity generation would accelerate the decarbonisation of electric sector, so that BECCS implementation is largely done and increase electrical production from biomass in the 2°C scenario with CCS.

Bioenergy triggers non-negligible reduction on CO2 releases

Measures and technological choice taken in climate policies scenarios aim at limiting global warming by reducing greenhouse gases emissions. Without climate policies, the model project 3200 GtCO2 cumulatively emitted in the rest of XXI st century. Limiting these emissions to 1600 or 900 or even 400 GtCO2 to avoid passing 3 or 2 or 1,5°C warming is very challenging. According to the majority of climate scientists and scenarios, "Two degree climate target [are] not possible without 'negative emissions'" [28] [39]. Modelling allows quantifying this reduction. In our study, while CO2 emissions are growing up from 32GtCO2 in 2015 to 40GtCO2 until 2050 and stabilize in baseline until 2100, they are decreasing after 2025 and passing under zero after 2060 in 2°C scenario with the possibility to add CCS technology (figure 4). The drop is more pronounced in the scenario without CCS in which carbon neutrality is required early, in 2050, because of no possibility to overshoot the warming limit without the use of BECCS and related negative emissions possibilities [START_REF]Fifth Assessment Report (AR5)[END_REF].

POLES modelling allows a separation of the causes of the CO2 releases. We see the contribution of bioenergy and the contribution of CCS devices in the effort for CO2 reduction (figure 4) in aggregate world emissions. Bioenergy uses by decarbonising uses thanks to fossil fuels substitution allow a CO2 emissions reduction up to 25% achievable in 2100. CCS allows also a smoother CO2 emission decrease, so a lower effort for world population in future years, but it requires a CO2 net capture up to 20% of current emissions level after 2060.

Figure 4: World CO2 emissions for the 3 scenarios considered, and the contribution of bioenergy and CCS in decarbonisation

Therefore, bioenergy develops largely and, combined with CCS, it gives a CO2 emission reduction feedstock up to one third of actual world CO2 releases. Because of their massive consumption, continent countries with dynamic economic activity are the key actors in GHG emissions, but also for large-scale development of CCS to provide large emission reduction: the USA, China and India represent half of the world CO2 captured with bioenergy potential. The European Union, Gulf countries and South Africa are the other main contributors for CO2 sequestration all around the world.

The different regional biomass allocation patterns

As seen in section 4.1., biomass potential is widely used throughout the XXI st century. Nevertheless, this global growth is not representative for each country of the world: each country has its own behaviour in biomass potential use.

4.2.1.

The use of local potential: some countries use their entire potential, others not

The biomass potential use in 2015 is shown on figure 5. This graph shows the repartition of the regions that use a particular percentage of their potential. For instance, 11 regions use between 0% and 5% of their bioenergy potential in 2015, and none use more than 75% of their potential. The average is about 25%, but some countries as China are already using more than 2/3 of their potential when others, such as Brazil or Russia, are using only very few percent of it. Hence, we can say that countries that are using the most of their potential nowadays is countries with low amount of potential compared to their needs.

However, world bioenergy use has increased and reaches 59% of the potential in baseline, 68% in 2°C scenario without CCS and 72% with CCS in 2050. We observe a higher biomass consumption in climate policies scenarios. Moreover, the spread of countries is lower: all countries use a higher share of their resource, between 45% and 85%. Some countries even saturate their potential by exploiting more than 90% of their resource. In 2100, we observe the same evolution of the trends highlighted before: global potential used has increased again and reaches 75% in baseline, 84% in 2°C scenario without CCS and 76% with CCS (figure 5). The evolution and the increase of biomass potential use is smooth in the XXI st century.

Figure 5: Representation of the number of regions based on their biomass resource potential utilisation-2015 and 2100

Nevertheless, it's worth noticing that Brazil, the principal exporter in XXI st century, has a typical behaviour because it doesn't use a massive proportion of its potential in 2050 (between 40% and 55%) and the growth is very high until 2100 (between 70% and 80%). Although Brazil increases self biomass consumption, this increase is due to the largest potential of the world and the increasing need for trade in the second part of the century.

The trade of bioenergy: biomass markets driven by few countries

After having highlighted the potential used throughout the world, let us consider the relatives trade concerns. Indeed, as some countries saturates almost their potential, they need to import the resource to fulfil their needs from the main biomass producers. Besides, some countries have huge quantities of resource, so that they can valorise it on the international markets at high prices.

In 2015, only few countries traded noticeable amounts of resource up to 0,5Mtoe. Only few actors are actives for large quantity of biomass trade: Brazil, Australia, Russia and Canada for exportation, and China, India and the USA for importation. The vast majority of countries have a negligible trade balance compared to these countries.

However, the development of modern valorization of biomass drive a huge increase in bioenergy consumption, (see part 4.1.). Larger quantities are traded on the markets with an increase through the XXI st century: quantities are multiplied by 10 in 2050 and again doubled in 2100 (figure 6). Our results show that the trends are conserved: the evolution of quantities traded are more or less linear for both importers and exporters. The same actors are the main contributors of bioenergy markets. However, while some countries had some negligible exports in 2015, we see that in 2050 and 2100 only the four main exportation countries are still exporting, in larger quantities. The others have almost saturated their potential and it is more cost effective to be supplied by foreign sources. In 2015, gasification is still in a R&D process, so that it is not developed worldwide. Methanisation represents the main technology installed to have a modern biomass valorization in energy. Nevertheless, thanks to some demonstrators, gasification will appear at the end of 2020s and will develop largely in the second part of the century [START_REF] Lambert M | Biogas: a significant contribution to decarbonizing gas markets?[END_REF]. The strong point of gasification is also to implement some CCS devices on factories so that net negative emissions can be reached.

Hence, at the end of the century, there is two main differences between baseline and 2°C scenario (figure 7). The first is the total installed power for bioenergy conversion that can triple in climate policies scenarios compared to baseline, that underline the importance given to biomass energy conversion in climate policies scenarios. Besides, while baseline drives to an installed power equivalent between methanisation and gasification plants, gasification is overriding with a mean of installed power that is ten times as much as methanisation in a climate constraint scenario. This trend is also accentuated with the possibility to add CCS because of these negative emissions well. Last, after biomass having an energy transformation process, the bioenergy produced is allocated for electricity production, biofuels and for heat valorization. In 2015, the valorization of biomass in mainly through heat and marginally in electricity. By seeing the XXI st century evolution (figure 8), three main points can be underlined. The first one is the decrease of the heat biomass valorization from 80% on average in 2015 to 30% on average in 2100 for all scenarios. This is due to higher added value with an electricty or biofuel valorization with the possibility to decarbonise these fields. We also observe the prominence of electricity use in climate policies scenarios and the prominence of biofuels transformations in baseline. Last but not least, we can see a large spread of the different consumptions for world countries. To sum up, we see a larger amount of electricity or heat in northern countries and a larger part of biofuel use in developing countries.

Conclusions

As solar, wind or hydro do not represent a silver bullet everywhere, there is no doubt that different types of biomass valorization such as direct use for heat and electricity or transformation in gas or biofuel is becoming more and more attractive for energy systems need decarbonisation coupled with flexibility. The environment of energy conversion routes is complex but the capture of interactions, complementarities and competitions between bioenergy processes and other energy technologies is a clue to better assess the future energy mix in line with the need of climate change stabilization [START_REF] Speirs | A Greener Gas Grid: What are the options?[END_REF].

POLES scenarios show that bioenergies will develop in all scenarios but with very different patterns as a function of country specificities and climate policies implemented. A larger part of the bioenergy potential from 25% in 2015 to 75-85% in 2100 is used worldwide whatever the assumptions. To go further, the amount of biomass used is multiplied by 5 from 2015 to 2100 in climate policies scenario. Hence, biomass is more used for decarbonization purpose. Secondgeneration biomass valorization plays a major role in electricity and fuels conversion especially with high value of carbon tax that penalise fossil fuels. At a local scale, we highlight that electricity and heat valorizations are prominent in northern countries while biofuels are prominent in developing countries that underline the different rates of development and the different needs each region has. Bioenergy penetration speed and rate depends on resource potential and technology maturities and cost effectiveness. Therefore, methanisation develops earlier than gasification because the process is more developed and (almost already) profitable. Nevertheless, gasification catches up methanisation thanks to a high resource potential and process progress expected in R&D. Hence, it will be used at the same order of magnitude in the second part of the century and even more in the presence of climate policies incentives and competitive CCS that are in development stages today. As seen in the last part of the analysis, CCS are becoming very important with high carbon constraints for technologies competitiveness in decarbonisation strategies. If BECCS are implemented widely, it will represent a key technology that generate negative emission needed for reaching climate commitments and would be massively used. Biomass methanisation and gasification will play an important role in future energy mixes bringing more stability and flexibility to the system and are complementary with variable renewable energies.

These two technologies enlarge the scope of flexibility brought by biomass to the gas vector [START_REF]World Energy Outlook 2017 Excerpt -Outlook for Natural Gas[END_REF]. Indeed, these processes convert biomass, which is a renewable, carbon-neutron source of energy, into a gas mixture that represents the new energy carrier. Then gas mixture from methanisation and gasification is transformable into heat, electricity or both or can be upgradable in biomethane for gas network injection with similar efficiencies. Yet, gas represents a good transition energy in order to reduce oil and coal use that can be stored more easily than electricity. It produces less local air pollution and the resource is cheap nowadays. Finally yet importantly, it is economically competitive for heating and electricity production as well as for mobility [START_REF] Iea | How to guide for bioenergy, roadmap development and implementation[END_REF]. Furthermore, this energy vector may be central in energy transition, as its production based on biomass could be completed with power to gas (P2G). This other green gas process uses an electrolyser and convert water in dihydrogen thanks to electricity. H2 is then upgradable in CH4 thanks to methanation [START_REF] Lambert M | Power-to-Gas: Linking Electricity and Gas in a Decarbonizing World?[END_REF]. Only few R&D demonstrators are in preliminary phase nowadays [START_REF] Ademe | Un mix de gaz 100 % renouvelable en 2050 ?[END_REF]. Although technical potential for this resource is estimated low in 2035 because of low level of maturity and insufficient VRE penetration, P2G is a way to store electricity surplus in the gas network. It would be very useful and competitive when VRE share will be important in connecting electricity and gas networks in order to avoid electricity curtailment [START_REF] Jm | A quoi sert le gaz?[END_REF].

However, large uncertainties concern CO2 emissions and costs for technologies aiming at decarbonising gas network. Indeed, estimations vary from -371 gCO2eq/kWh to 642 gCO2eq/kWh for green gas production from biomass, with an associated technological cost between 50€/MWh and 200€/MWh [START_REF] Li H | Biomethane production via anaerobic digestion and biomass gasification[END_REF][START_REF] Gotz M | Renewable Power-to-Gas: A technological and economic review[END_REF]. These figures depend on the scope taken into account in the assessment (for example the perimeter of life cycle analysis for bioenergy CO2 releases estimation), the type of technologies tackled, the localisation, R&D and maturity hypothesis, etc. Moreover, costs for equipment and network needs for adaptation are also uncertain. For example, it is hard to consider what will be the cost for the gas network to switch from a fossil fuel supply to a green gas supply with the possibility to build some devices such as electrolysers only dedicated to produce green gas. Hence, even with our scenarios and hypothesis, competitiveness, future development and deployment for these energetic routes are almost unpredictable precisely [START_REF] Ademe | Actualisation du scénario énergie-climat -ADEME 2035-2050[END_REF]. At the end, "technology is not enough", because the field competitiveness in decreasing CO2 emissions depend a lot of the behaviour of people for technology use. Besides, negative side effects for bioenergy can slow the development of associated technologies where methanisation and gasification plants can raise concern for local population. They could be afraid of noise, smell, explosion risk, increase of traffic to fuel digesters, flies invasion, etc. All in all, a better communication about the characteristics of these technologies and how is it implemented in lands is key to remove concerns and to launch these fields at a bigger scale.

Figure 1

 1 Figure 1 sums up the biomass feedstock feature, on the left, and the different routes and possibilities for energetic valorization.

Figure 6 :

 6 Figure 6: Repartition of principal actors on biomass markets -3 scenarios ranges

Figure 7 :

 7 Figure 7: Total installed power for gasification and methanisation

Figure 8 :

 8 Figure 8: The world repartition of bioenergy uses

Figure 9 :

 9 Figure 9: Sankey diagram, biomass use in 2100 -baseline -China, the USA and Brazil

Table 1 : Overview of methanisation installations Actors Inputs Construction cost (€/kW built)

 1

	Farms	Vegetal, animal and sewage wastes Wood residues	10000-15000 (35kWe) 8600 (170kWe) 5600 (500kWe)
	Non-hazardous landfill	All wastes excepted electronics	1700-2300 in cogeneration
	Industries	Industrial wastes	15000-76000
	Domestic wastes	Domestic wastes	18000-42000
	Water treatment plant	Plant sludge	13000-20000 (<10000 habitants) 3300-6600 (>100000 habitants)
	Municipal wastes	Municipal wastes such as grass	860-2000
	It is worth noticing that the energetic yield for this bioenergy conversion from biomass to biogas is between 55% and
	75% [15]. Nevertheless, other coproducts can be valorised, for example the solid residue called digestate into fertilizers,
	which can raise the global profitability of the process. While there are some technologies developments elsewhere (for
	example in Brazil and the USA), most of these developments are taking place in Europe, with Germany taking the lead.
	Since 2009, the amount of biogas plants in Europe grows by 20% per year on average. This number comes from 6227 in
	2009 with 8,3Mtoe energy produced to 17376 (+279% in 6 years) in 2015. Germany is the engine of Europe with 10846
	plants installed in 2015, then Italy (1555 plants in 2015), France (717) and Switzerland (638) [16]. Figures are not
	consolidated for 2016 and 2017, but the number of biogas plants in Europe are raising a lot and policies objectives are
	high for 2020, 2023, 2025 or 2030 milestones in the decarbonisation strategies

Table 2 : Overview of gasification installations Type of gasifier Target size and applications Drawbacks Advantages

 2

	Downdraft -fixed bed reactor (75% of gasifiers installed)	Small scales up to 1,5MW Application : electricity	Poor control of temperature, mass and heat transfer Inefficient char removal	Simple and robust Biomass flexibility in input Low tars
	Updraft -fixed bed reactor (2,5% of gasifiers installed)	Upper scales up to 2,5MW Application : heat	Poor control of temperature, mass and heat transfer Production of lots of tars	Simple and robust Biomass flexibility in input
	Fluid bed reactor (20% of gasifiers installed)	Medium scales up to 25MW Application : electricity	Biomass moisture requirements Ash management	Efficient control of temperature, mass and heat transfer Easy to scale up
	Entrained Flows Reactors : (2,5% of gasifiers installed)	Large scales up to 100MW Application : liquid fuels	Complex Expensive	Compact No tars and char Ash management

Biomass energetic conversion reach a yield between 60% for direct biomethane network injection and 90% with biomethane + heat production while biomass boilers have a energetic yield reaching 80-85%.

The time horizon took into account for comparison with the other models is 2040. Indeed, each study does not present its result for the same horizon, so that we have to take a reference where all three models propose quantified results. This milestone is valuable for analysis because effects of some climate policies and changes in energy paradigm can be seen and projected.

Acknowledgments

Funding for this research was provided by a grant from Carnot Energies du Futur.