Paritosh Sharma
email: paritosh.sharma@lisn.upsaclay.fr

Michael Filhol
email: michael.filhol@cnrs.fr

Multi-Track Bottom-Up Synthesis from Non-Flattened AZee Scores

Keywords: AZee, sign language, avatar

We present an algorithm to improve the pre-existing bottom-up animation system for AZee descriptions to synthesize sign language utterances. Our algorithm allows us to synthesize AZee descriptions by preserving the dynamics of underlying blocks. This bottom-up approach aims to deliver procedurally generated animations capable of generating any sign language utterance if an equivalent AZee description exists. The proposed algorithm is built upon the modules of an open-source animation toolkit and takes advantage of the integrated inverse kinematics solver and a non-linear editor.

Introduction

Sign language synthesis is a technique for converting a sign language utterance description into an avatar animation. Such avatars are commonly referred to as signing avatars. Automating this process can provide a flexible way to generate sign language content while preserving the signer's anonymity. This also provides means to customize the sign language content more conveniently than fixed video recordings of signers. Various systems for sign language synthesis have been developed over the years. Most of them relied on descriptions that modeled sign language utterances as sequences of glosses. This approach has several limitations ranging from synchronisation to contextual variations of signs. Hence, various utterance representations have been developed over these years to address one or more of these problems. EMBRScript [START_REF] Kipp | Sign language avatars: Animation and comprehensibility[END_REF] added timing information to these sequences of glosses. The P/C model [START_REF] Huenerfauth | Generating American Sign Language classifier predicates for English-to-ASL machine translation[END_REF] solves the problem of synchronisation and concurrency of signs by allowing for partitions in utterance descriptions. The ATLAS project [START_REF] Lombardo | A virtual interpreter for the italian sign language[END_REF][START_REF] Bertoldi | On the creation and the annotation of a large-scale Italian-LIS parallel corpus[END_REF] addresses the issue of sign variations using modifiers. Finally, the HLSML model [START_REF] López-Colino | The synthesis of lse classifiers: From representation to evaluation[END_REF][START_REF] López-Colino | Spanish sign language synthesis system[END_REF] addresses the issue of timing information and sign variations. Unlike those mentioned above, the AZee model [START_REF] Filhol | Nonmanual features: the right to indifference[END_REF] allows us to write parameterised signed forms for semantic functions. A sign language utterance is encoded in the form of a hierarchy of applied production rules instead of a sequence. Given a description, it produces a timeline specifying all parts of the utterance to render with the avatar, thereby addressing the issues of non-manual features synchronisation, sign concurrency, and timing. Furthermore, AZee's timeline specifications also carry interpolation information and are essential for synthesising the utterance. These features of AZee are essential for our work since modern animation systems use a multi-track timeline and allow for non-linear editing of animation blocks. This paper aims at synthesising AZee input with such type of software, namely Blender in our case. We first present two prior approaches complementing each other that worked on animating from AZee, and explain a fundamental limitation found in one of them. We then propose a novel algorithm to animate AZee descriptions that allow for better synthesis. Lastly, we present our implementation in Blender and snapshots of output results we were able to generate.

State of the Art

To animate AZee, [START_REF] Filhol | Synthesizing sign language by connecting linguistically structured descriptions to a multi-track animation system[END_REF] follow a fundamental guiding principle, according to which the coarser the basic animation blocks, the more natural the final animation. To apply this principle, we should try to work from coarse AZee blocks as much as possible and fall back on synthesising from lower levels of AZee specification only if necessary. If this top-down search in the hierarchy of the AZee expression is not attempted, or indeed if it reaches the bottom of the hierarchy, the animation needs to be built from the bottom-up, i.e., work from the minimal articulatory constraints provided by AZee in its block specifications. In this section, we first review the Paula system, the only one attempting a top-down search for synthesis from an AZee description. Then we look into a Blender implementation, the only one proposed for a bottom-up synthesis of AZee.

Top-Down Approach

The Paula sign synthesis system provides a multi-track animation system close to how AZee describes sign language utterances. The system uses multiple animation techniques, capitalising on their strengths. Currently, it principally relies on pre-animated clips made by artists whose work is made simpler by using procedural techniques such as spineassisted computation [START_REF] Mcdonald | An automated technique for real-time production of lifelike animations of american sign language[END_REF]; hence they do not have to be an expert in keyframe animation or armatures. These clips, representing coarse animation blocks, are essential in encapsulating the natural motions [START_REF] Mcdonald | A new tool to facilitate prosodic analysis of mo-tion capture data and a datadriven technique for the improvement of avatar motion[END_REF] which are vital to improving sign language generation. Furthermore, the system has been extended to enable natural proform synthesis [START_REF] Filhol | Extending the azeepaula shortcuts to enable natural proform synthesis[END_REF]. Various extensions have been made for better facial model synthesis [START_REF] Wolfe | State of the art and future challenges of the portrayal of facial nonmanual signals by signing avatar[END_REF]. Overall, this gives a more natural animation since it encapsulates movements that would be natural to a human signer. All of this is done on a multi-track animation timeline.

Using coarse blocks improves natural synthesis. However, it relies on a large set of shortcut clips, and does not address solving minimal constraints in the case none exists for a given segment.

Bottom-Up Synthesis: Building from Minimal Constraints

In contrast, a bottom-up approach proposes working from small articulation constraints and then combining and evaluating them to generate an animated utterance on a timeline. Thus, while it generates motion that looks more robotic, it can generate any sign language utterance description, and therefore give complete coverage of the AZee language description. This method of synthesis from AZee was most recently attempted by [START_REF] Nunnari | Animating azee descriptions using off-the-shelf ik solvers[END_REF].

To understand it better, let's consider the AZee expression nicht-sondern(arbre, armoire) from their work, which means "not a tree but a wardrobe." Evaluating this expression with the AZee interpreter yields a set of time-bounded intervals arranged on a timeline. These intervals can be represented as blocks on a horizontal axis such as those shown in Figure 1. This arrangement is called an AZee score. Each of these intervals contains articulatory constraints such as, placements (e.g. place fingertip at forehead), orientations (e.g. orient forearm along upward vector), transpaths (e.g. fingertip must transition on a circular path) and holds (e.g. hold block UNIT0 for a duration).

In such a score, we notice that these constraints can apply simultaneously Figure 2. For example, PALMS DOWN, which refers to the orientation of palms downwards, while HANDS CONTACT, which refers to the contact of palms.

Since both these blocks affect common bones of a bone chain, animating them separately is a problem.

To avoid this problem, Nunnari et al. chose to flatten the AZee score to create a linear sequence of keyframes comprising of,

• the constraints corresponding to the boundaries of the original blocks (example k 1 , k 2 in Figure 1)

• additional keyframes to control interpolations as specified by transpaths (example k 12 , k 13 , ...

k 18 in Fig- ure 1)
Each of the former kinds contains all of the articulatory constraints applied at that time, collecting from any block starting, ending, or crossing over that keyframe. A keyframe of the latter kind contains the same, plus the additional place constraints generated by the transpaths.

When flattening, all the underlying constraints within the blocks are projected on a single timeline. For example, in Figure 1, the constraints in PALMS DOWN and HANDS CONTACT are combined to make one single set of constraints for the keyframe k 9 . This flattened score is then used to animate the posture. This is done by resolving the sets of constraints associated with each keyframe in chronological order on the timeline. The constraints are then eventually resolved into the rotation of bone joints. Thus, a posture with n bones can be represented as the following:

X(i) = {bone rot 1 (i), bone rot 2 (i), ..., bone rot n (i)}
where X is the state of the posture for the i-th frame.

A problem with this approach is that, even though the system fixes the issue of concurrent constraints depending on each other, it loses the information brought by the parallelism of the blocks while flattening. This means that the only information we have for interpolation are the constraints present on k 1 , k 2 , ..., and so on. Moreover, every interpolation between each pair of successive keyframes will be distributed on all the bones, including those that should not be affected. Thus, we lose the dynamics of the present blocks, and there is no information on how the system should interpolate amongst these flattened constraints. Also, even if the concurrent blocks comprised of constraints not affecting the same bone chains, there was never a need to flatten in the first place.

In the following section, we propose to fix this using an algorithm that does not flatten by presenting a multi-track bottom-up synthesis of an AZee description.

Algorithm for Multi-Track Synthesis

We aim to build a multi-track system without flattening the AZee score. Our work focuses on synthesising the nonflattened AZee score in Figure 1. Since the score is constructed based on linguistic descriptions which can be nonlinear, we need to impose a certain set of rules while constructing the multi-track timeline, which were previously resolved by flattening the score. Similar to the previous work, we focus on placements and orientation constraints. However, since we are not flattening, the transpath and hold constraints will not be resolved, and we have to deal with them separately.

Resolving Conflicting Cases

We chose to resolve the conflicting cases by applying the following rules. Problem: Time overlapping blocks containing constraints that act on the same bone chain but do not start at the same time. For example, in Figure 3, HANDS APART shouldn't be evaluated before PALMS DOWN.

Rule 1: Timely Evaluation

Response: In this scenario(Figure 3), the evaluation of HANDS APART has to account for the fact that the palms are already facing downwards since both blocks act on the same kinematic chain. Thus, to fix this, time overlapping blocks acting on the same bone chains have to be evaluated chronologically. Response: In this scenario(Figure 4), the evaluation of PALMS DOWN has to account for the fact that the hands are already in contact. Thus, to fix this, precedence has to be given to the block containing placement constraints over those with orientation constraints.

Rule 3: Second Pass for Transpaths

Problem: A block contains a transpath constraint.

Response: The transpaths represent transitioning of the posture along some path for an effector of the body. It depends on the evaluation of surrounding blocks and all subsequent interpolations. The solution is, therefore, to evaluate blocks containing transpaths in a Second Pass(Figure 5) after all other blocks have been animated.

Rule 4: Second Pass for Holds

Problem: A block contains a hold constraint.

Response: A block containing the hold constraint specifies that constraints of some other block have to be held for a duration. It, therefore, depends on the animation of that reference block. Hence, blocks containing holds have to be evaluated in a Second Pass (Figure 5) as well.

Non-Conflicting Cases

Any case not mentioned above will be clear of conflicts and can be evaluated independently. These include:

• all blocks not overlapping each other on the timeline;

• overlapping blocks that act on different bone chains;

• other constraints such as morph and look act independently from the others.

Implementation and Experimental Results

The above system has been implemented as an add-on in Blender(v3.1). The interface (Figure 6) shows the Blender interface configured for AZee synthesis. Its main components include:

AZee editor (a) An editor to evaluate AZee expressions. It also includes settings for armature configuration, toggling constraints, and managing body sites. Properties (d) Modify inverse kinematics (IK) settings, access pose library, and animation layers.

To implement IK solving, we chose to use the iTaSc IK solver [START_REF] Smits | itasc: a tool for multi-sensor integration in robot manipulation[END_REF]. The reason for that choice is its popularity and that it is already integrated into Blender.

Our implementation is still under development, but the current state of progress already allows to visualise timelines and extract renders, as shown in Figure 7. Here, we present various synthesised AZee descriptions such as :bien, :armoire, :arbre, :bonjour. The current implementation produces satisfactory utterances of simple descriptions but needs more testing and debugging for complex utterances. These occur mainly when there joint orientations get close to the rotation limits. This can be observed in :armoire in Figure 7 where the hand rotation limits are reached to satisfy the orientations and placements. But we see that the linguistic constraints on the forearm, hand, and finger orientations, for example, are well satisfied. As a result of not flattening the score, we preserve the dynamics of individual blocks. This can be seen in armoire comparison.mp4 available at https://doi. org/10.5281/zenodo.6563373 where (A) shows an :armoire synthesised using a flattened score while (B) shows the one synthesised using our approach. For (A) we observe that the interpolations are distributed on all bones while for (B) they distribute only over the relevant bones of the blocks shown in the Non-linear Editor.

Conclusion and Future Prospects

In this work, we proposed an algorithm that allows for developing the first multi-track animation system for AZee bottom-up synthesis. This proposed algorithm is a step forward in sign language synthesis, allowing for individual AZee blocks to be synthesised independently and ensuring that the dynamics of these blocks are preserved by not flattening. We also integrate our algorithm as an add-on in the open-source Blender software. Eventually, we want to integrate our work with a topdown technique to have a complete hybrid approach to animate AZee descriptions. The implementation should allow shortcuts using pre-animated clips, MoCap data, or processes that animate these blocks. This would create a system leveraging the advantages of both techniques, as proposed in the AZee-Paula effort.

The current system doesn't resolve AZee morph constraints. More research is needed to handle the bottom-up synthesis of morph constraints and integrate it with our current work. Furthermore, the AZee constraint dependencies can eventually be mapped as a dependency graph [START_REF] Zhang | Write-ananimation: High-level text-based animation editing with character-scene interaction[END_REF][START_REF] Watt | Libee: A multithreaded dependency graph for character animation[END_REF] which can be solved using a multi-pass system. Lastly, this work can be extended to make the bottomup synthesis less robotic using ambient noise analysis and style transfer techniques [START_REF] Holden | Fast neural style transfer for motion data[END_REF].

Acknowledgement

This work has been funded by the Bpifrance investment "Structuring Projects for Competitiveness" (PSPC), as part of the Serveur Gestuel project (IVès et 4Dviews Companies, LISN -University Paris-Saclay, and Gipsa-Lab -Grenoble Alpes University).

Figure 3 :

 3 Figure 3: Timely evaluation

Figure 1 :

 1 Figure 1: Arrangement of blocks in an AZee score(top) and the equivalent flattened score(bottom)

Figure 5 :

 5 Figure 5: Second Pass to resolve transpaths and holds for :nicht-sondern(:arbre, :armoire)

Figure 6 :

 6 Figure 6: Main Blender interface. (a) AZee editor. (b) 3D Viewport. (c) Non-linear Editor. (d) Properties panel.

Figure 7

 7 Figure 7: Results


For many poor people, finding a way out of poverty is limited by their inability to access finance (save, borrow or insure) due to limited infrastructure, constraining regulatory and legal environments, limited understanding and awareness of financial services and perceptions among others that lending to or insuring them is risky and commercially unviable.

Building inclusive financing systems offers a route out of poverty and system traps characterized by persistent poverty, limited economic opportunities and poor health and education. People, particularly the poor and women, can be economically and socially empowered through enhanced access to well-functioning and efficient financial services, allowing them to better integrate with the country's economic activity and contribute to economic growth.

Microfinance is one of the most significant innovations in development policy of the past three decades. Providing microfinance services to the poor (those earning less than USD 2 a day) has been hailed by advocates as an effective poverty alleviation and human development tool. 1 It provides poor and marginalized households with access to financial services including credit, savings and insurance by linking them to the formal banking and insurance sector. It facilitates income-generating self-employment activities, financial risk protection and access to essential education and health services. It enables micro-entrepreneurs to build businesses and increase their income-and improves the general economic well-being of the poor and marginalized. It enhances financial outcomes of the poor such as savings and asset accumulation.

At the same time it generates nonfinancial outcomes such as health, food security, nutrition, education, women's empowerment, housing, job creation and social cohesion. 2 Microfinance is thus considered a powerful tool to build human capital in developing countries, especially in Africa. The Human Development Report 2005 attributed Bangladesh's relative success in human development to microfinance that had expanded economic opportunities, particularly for women, providing them with greater empowerment and choice. 3 However, as microfinance is still evolving, its impact on human capital development is not yet known or fully realized.

Given its current progress, microfinance can be viewed as a step closer to taking formal financial services to the poorest. Its current beneficiaries are mostly the middle class and marginal poor. The poorest are left behind, less involved in microfinance and less able to move to a sustained labor and consumption pattern. Perhaps, the next logical step would be to make it suitable for the poorest so that financial inclusion is universal. There is also an increasing need for more systematic evidence of the positive link between microfinance and human capital development.

Starting from these observations, the objectives of this chapter are threefold. First, it seeks to provide a conceptual framework summarizing the theoretical links between microfinance and human capital development. Second, it extends the systematic review of van Rooyen, Stewart and de Wet (2012) of microfinance impact in Africa by including works related to North Africa and focusing mainly on human capital development. Third, it discusses key policy issues and strategic directions in order to increase the human capital effects of microfinance in Africa. [START_REF] Van Rooyen | synthesis of what works in microfinance in Sub-Saharan Africa and their financial and human capital effects[END_REF] What is microfinance?

Microfinance has emerged largely as a community or nongovernmental organization (NGO) response to market and government failures. Formal banks fail to provide services to poor clients, and governments are unable to ensure that needed financial services reach the poor. It is a promising way to allow the poor and disadvantaged to save, as well as to provide them with access to credit to improve their income-earning potential. Indeed, it has become an important tool in fighting poverty.

Microfinance is made up of a set of small financial products that include savings, credit and insurance, and that suit people with low incomes and who live in places where the financial system is not so well developed. These people were initially excluded from the classic or formal banking system. A review of data from 47 countries reveals that microfinance services are provided by large networks of development banks, big institutions (each reporting more than 100,000 clients) and numerous small institutions that vary by size, degree of organization and legal status. The first two categories of institutions, which are either state-sponsored or not-forprofit institutions (NGOs, associations, mutual insurance companies/cooperatives, limited companies) meet about 90% of the demand, particularly from the poorer population groups. 5 Some institutions, primarily engaged in other businesses, extend microfinance as a means to generate community goodwill so as to enjoy a competitive edge through the banking-social sector linkage. Altogether, microfinance resources are estimated to constitute about 2.5% of total banking resources. The microfinance markets appear to be greatly segmented and with weak links among market segments. Funds of different lenders can hardly be substituted for one another. As a consequence, deposits mobilized-as well as credit facilities-differ in structure, associating demand with distinct socioeconomic groups.

Microcredit

The most common microfinance tool is microcredit, which extends small loans (USD 50-1,500), often without collateral, to impoverished people. Microcredit provides loans at relatively low interest rates that could be used to generate income or be used to pay for services. First explored in Bangladesh in 1976 when the Grameen Bank was established, microcredit substitutes borrower collateral with social collateral through peer pressure support using small informal groups (largely female) to ensure repayment. The essence of microcredit lies in its lowcost procedures replacing sophisticated credit-evaluation techniques and collateral regulations. Loan interest rates (18-61% a year), while seemingly high by commercial credit standards, are generally far below rates (reportedly 36-5,000%) charged by informal credit sources such as money lenders in the same settings. 6 The most recent estimate of the median size of a microloan per borrower ranges from USD99 in South-East Asia to USD 1,254 in Europe (table-1).

Micro-saving

Micro-saving offers a safe, convenient and accessible saving platform for the poor and introduces sound financial, organizational and institutional practices that align with household needs. Through micro-saving, the task of smoothing consumption is made easy for those, particularly women, who are unable to store money safely otherwise. Developed in the 1990s, it allows people to deposit money in small amounts for future use, without minimum balance requirements and often in conjunction with credit. Saving by the poor often comes in such small increments that conventional banks are not prepared to offer services to them, either because the transaction costs are unprofitable or because of other more lucrative investment opportunities. Although micro-saving does not change the need for people to pay service providers (such as health care) when they get sick, it is a form of prepayment. Micro-saving accounts work like any formal saving account in a bank, but are designed to accommodate smaller deposits. 7 The minimum balance requirements are often waived, or kept low, allowing users to save small amounts.

Micro-saving encourages poor people to save for education, health care or other future investment. People who participate are usually better prepared to cope with any financial risks, which would otherwise force low-income individuals to borrow at high-interest or suffer from lack of service access. But the ability to save through a microfinance institution (MFI) is often limited to people who are willing to borrow from it. A survey of 166 MFIs in 2009 revealed that only 27% of MFIs offered saving products. 8 The total number of deposit-taking nonbanking financial institutions was 40,000 in 2011 even while the number of commercial banks declined from 16,000 in 2004 to 14,200 in 2011; 9 20% of them were in low-income countries. In Burundi there were 124 deposit accounts per 1,000 adults in nonbanking financial institutions against 32 deposit accounts per 1,000 adults in commercial banks. 10 In an attempt to boost micro-saving coverage, the Bill & Melinda Gates Foundation offers grants worth USD38 million to 18 MFIs in Africa, Latin America and South Asia.

5

Micro-insurance

Micro-insurance relates to risk pooling and financial risk protection. Although it is risky for MFIs with a smaller risk-base to offer insurance products, as larger risk pools facilitate better risk pooling, MFIs do provide them to cover health and agriculture risks. An insurance product can be termed micro-insurance if it targets low-income or excluded populations, and the premium is adjusted based on affordability. 11

What does microfinance offer to human development?

Microfinance impacts human development through six complementary instruments (figure 1): women's empowerment; income generation; education; employment creation; social and financial inclusion; and access to services. Several MFIs have sought to provide additional inputs to their financial products such as basic health services, health education or health insurance products. 12 "Such integrated package may provide both the means (income/empowerment) and the knowledge to address priority health concerns, and present the possibility of substantial cost recovery from microfinance providers." [START_REF] Pronyk | [END_REF] Studies suggest these models can lead to higher immunization rates, the adoption of healthy breastfeeding practice and better management of childhood diarrhea. 14

Education and skills building

Despite a huge change in the educational system in Africa, enrollment in rural areas and fragile states is still lagging2 . Microfinance has strong potential impacts on education and enrollment, especially in primary schools.

According to Maldonado and Gonzaléz-Vega (2008), microfinance may influence education through five channels. First, access to microfinance influences the household income. Given the positive income elasticity of the demand for education, we expect an increase of education demand especially in rural areas. Income-generating activities from microfinance permit higher investment in education. Second, income volatility does not allow sustained enrollment over time. Microcredit can help stabilize household revenue and avoid child labor in order to increase household revenue. In fact, most rural African households take their children out of school in such periods.

Third, women are supposed to have stronger preferences for educating their children than men; microcredit is meant to benefit women more and change their power in their households. Therefore, the education of children is improved by this effect. Women may dedicate more time to educating their children than alternative activities. "If preferences are gender-related and microfinance improves direct access to loans by women, thereby changing their power to influence household decisions, the rate of human capital formation may be affected (gender effect)." [START_REF] Maldonado | Pitt and others[END_REF] Fourth, since microcredit improves the opportunities for educated household members and allows them to realize higher incomes and wages, the incentive for education will be strengthened. Incentives for education may be transmitted from one generation to another. This effect may be observed in the long term but not in the short term.

And fifth, microcredit may change demand for child labor within households and allow more time for education. The poor are used to involving their children in revenue-generating activities, even labor, because of lack of revenue. Because microfinance may allow sustainable revenues, the incentives to use child labor may change.

These five potential effects work together to improve the education outcomes (enrollment rates, time spent on education and knowledge acquisition). Several international reports, especially for Asia, Latin America and Central Europe, demonstrate such effects.

Access to essential services

Microfinance has the potential to make essential health services more accessible and to allow people to either save for future needs or to repay the costs slowly over time. By facilitating early case detection through timely financing of outpatient care, it can minimize risk of hospitalization, thus reducing costs, and allow people to continue working.

The interdependence of poverty, health and development might seem obvious, but crosssectoral experience on how and where to intervene remains limited. Microfinance is at the center. Access to financial services allows rural and poor persons to borrow and save and can change their behavior toward health care. They can pay for medicines and clinics visits more easily. MFIs are used to operating in slums, villages and neighborhoods where poverty and unemployment are high. Combining health and financial interventions can be powerful. Pronyk, Hargreaves and Morduch (2007) discuss the potential of MFIs to contribute toward achieving the Millennium Development Goals, especially those involving health.

At least four channels are mentioned to discuss the link between microfinance and health for the poor. The first is linked to the section below. Improving education access can have profound effects on health and well-being (lower rate of illness, malnutrition and childhood mortality). Access to primary schools is sometimes accompanied by free nutrition programs for under-or malnourished children, as in Mauritius and many other countries.

The second channel is nutrition. Increased access to microfinance can raise food consumption, especially for children. Microfinance increases nutritional outcomes. Several studies show that in Bangladesh significant effects on nutrition, especially for children between 6 and 72 months old, have been reached following microcredit initiatives. 16 Helping the poor generate sustainable revenues improves their access to healthier nutrition and by this process improves their children's health.

The third channel is linked to the gender effect. Evidence supports a positive correlation between microfinance and enhanced health through improvement of social status of women. As women become active and more financially sustainable, they tend to improve their health conditions.

The fourth channel is linked to empowerment of women, which leads to less violence and HIV/AIDS infections.

Microfinance programs allow the poor to have better attitudes and reflexes about their health, especially the health of their children. One therefore expects to see a decrease in child mortality and improved sanitation conditions in general.

Financial inclusion and risk protection

Advocates of microfinance argue that access to finance can help substantially reduce poverty. Microfinance has been recognized as one of the most efficient tools to alleviate poverty, considering its significant contribution to job creation and revenue generation for a government. [START_REF][END_REF] This is due to the empirical finding that accentuated the vast potential of microfinance in improving the quality of life of poor people. [START_REF]Some of the numerous studies on microfinance include Khandker and Pitt[END_REF] Given this, microfinance has become a prominent tool for poverty reduction, especially in Malaysia, Indonesia, Bangladesh and the Philippines. The contribution of microfinance to reducing poverty has convinced many governments, NGOs and individuals to support MFIs and their activities. Longitudinal studies from Bangladesh have found an association between poverty reduction and greater consumption attributable to microfinance participation.

One of the main criticisms of microfinance is the claim that it does not reach the poorest of the poor 19 or that the poorest are deliberately excluded from microfinance programs. 20 The core poor are generally too risk averse to borrow for investment in the future. They benefit only to a very limited extent from microfinance. Even when they do benefit from microfinance the most important part of the loans is dedicated to immediate consumption. An increasing objective of sustainability pursued by MFIs leads them to exclude the poorest and to focus on the "high fringe" of the poor and on the middle class. Only subsidized programs have tried to reduce poverty and to include them. The tradeoff between outreach and sustainability is becoming obvious worldwide. 21

Women's empowerment

Microfinance has been celebrated for its ability to reach out to women and enhance their welfare, offering them the possibility to move toward entrepreneurship and to abolish the poverty circle. While microfinance does not target women only, they form the majority of beneficiaries. At the end of 2005 it was reported that more than 3,000 MFIs were active and provided services to more than 113 million people worldwide, 84% of them women. 22 Women typically benefit from smaller loans than men. 23 D'Espallier, Guérin and Mersland (2010) survey performance data of 350 MFIs in 70 countries over 11 years and confirm that female borrowers are associated with better credit risk. One of the explanations consists in affirming, according to multilateral development agencies and MFIs, that women are good credit risks. The World Bank (2007, 124) argues that "experience has shown that repayment is higher among female borrowers, mostly due to more conservative investments and lower moral hazard risk." Some authors like Armendàriz and Morduch (2005) recommend targeting women to reduce the default risk for MFIs. Several empirical studies have found that women are more conservative or cautious in their investment strategies and thus have better repayment records. Another argument is that women have fewer credit opportunities than men and must repay their loans to ensure continued access to credit. Agier and Szafarz (2013) show that in Brazil, there is no evidence of gender bias in loan attribution by MFIs and the hypothesis of "ghetto-izing" women with small loan programs is invalid. Since microcredit is benefiting women's empowerment and entrepreneurship, this leads to several human capital improvements in the household like better education for their child, better health and more investment in social activities (building social capital).

This discussion shows that microfinance has a great potential in theory to develop human capital in Africa by targeting the poor. It improves their financial, economic and social inclusion in such a way that increases their education and skills. As the development of their human capital increases their employability increases, permitting better jobs and lives. This process also allows the creation of new businesses. Microfinance is considered nowadays one of the main policy tools to reduce poverty and boost human capital. The empirical literature has showed such impacts in Asia (particularly in India, Cambodia and Indonesia) and Latin America (Venezuela and Peru). Since they were first adopters of this financial technology, their experience is very useful for Africa.

But at least four issues remain over the link between microfinance and human capital formation worldwide. First, microfinance has not exceeded its borders to include the poorest. 24 Finding an appropriate way to target the poorest is still open and needs innovative solutions. Second, pushed by the need to be sustainable, many MFIs today are targeting the middle class. As many workers are poor (the working poor) and some middle class people are restricted in accessing credit and finance, they are asking for loans from MFIs. They consider MFIs as competitors of classical finance institutions with higher interest rates and easier loan conditions. As the target population for MFIs is changing, this may induce MFIs to prefer them over the initial target population.

Third, as most MFIs are financial institutions, cooperatives or NGOs, some are operating without the necessary skills. This emerging industry needs skilled employees for better performance. While the industry has existed for more than four decades, few higher-education institutions propose degrees and training in it. This is particularly true in Africa, which has a huge need for skills development and professional training in this industry. And fourth, the paradox is that the poor use MFIs more for saving than credit, to adjust their savings and smooth consumption over a longer period.

Microfinance in Africa

Despite serious structural obstacles such as lack of infrastructure, currency stability and high inflation, Sub-Saharan Africa recorded the largest increase in microcredit of any region, surpassing the South Asian portfolio for the first time since 2006. 25 Moreover: Sub-Saharan Africa looks set to be the fastest growing market over the next couple of years and perhaps beyond. This is good news for a continent that has some of the lowest financial inclusion levels, but is enjoying the political stability and demographic dividend that may provide a strong base for solid economic growth in the coming decade. With the right market conditions, this may be an excellent opportunity for microfinance investment, but here again it's worth noting that current growth in the continent is taking place in a relatively small number of countries and regions. 26 Microfinance is still evolving and the actual number of poor people linked with formal banking institutions through micro-banking is not yet known. Estimates of the number of savings and credit accounts with alternative financing institutions in 2004 indicated about 650 million micro accounts serving about 500 million clients worldwide, with a predominant (84%) presence in the Asia-Pacific region. Demand-side estimates indicate that microfinance has so far reached between 2% and 13% of the target (that is, the poor) population in different settings. Some institutions, primarily engaged in other businesses, extend micro-banking as a means to generate community goodwill so as to enjoy a competitive edge through the banking-health linkage. Altogether, micro-banking resources constitute about 2.5% of total banking resources.

Despite structural problems and deficiencies, microfinance is progressing in Africa. Many funds and MFIs are entering this market for the first time despite the serious problems that still remain. Sub-Saharan Africa is still a difficult environment, with the less developed countries and Tier 2 and Tier 3 MFIs remaining inaccessible due to lack of market infrastructure. Aryeetey (2005) reports that microfinance markets in Africa face several market failures. The most important one is the lack of market integration. As a result of fragmentation, and because each lending unit cannot alter the structure of its operations and products in the short to medium term without additional flows of resources, there are few lenders in Sub-Saharan Africa that meet the needs of borrowers. In Côte d'Ivoire, Togba (2012), using 2002 data, found that MFIs collect a much larger amount of savings than what they give out in loans, and that informal loans through microcredit institutions have a particular role to play in the financial sector in that country. Ethnic networks, especially religious ones, are important. Microfinance can foster the human capacity to accelerate Africa's economic transformation and to move to more inclusive and green growth. In fact,

Access to finance may contribute to a long-lasting increase in income by means of a rise in investments in income generation activities and to a possible diversification of sources of income; it may contribute to an accumulation of assets; it may smooth consumption; it may reduce the vulnerability due to illness, drought and crop failures, and it may contribute to better education health and housing of borrower. … Microfinance may have positive spillover effects such that its impact surpasses the economic and social improvement of the borrower. 27

Self-funded groups are an increasingly common way of delivering microfinance services. In Africa several international NGOs are promoting village savings and loans associations as member-driven, local institutions. Although micro-insurance is growing very fast in Africa, its coverage of lives and properties in still low (table-2). According to a survey in 2011, South Africa accounted for 60% of the insurance coverage in Africa. 28 The coverage is low in Africa despite the large number of community-based initiatives; formal commercial insurers accounted for 78% of the lives covered in Africa. To illuminates potential reasons for the low take-up of microfinance. The Gallup World Poll asked if they are aware of any institutions in their community that help people obtain small business loans, 16.2 percent of the responders stated that they had never heard of such institutions (box-2). An additional 46.2 percent stated that such institutions are not available in their community. Certainly, a percentage of those reporting that microcredit was unavailable in their community might also simply be unaware of its presence.

Scope for human development through microfinance

This section summarizes the key empirical findings in the area of human capital impacts of microfinance. Our main concern is to address the question of "what works?" We survey the main findings of the literature and try to identify the trends and best practices. But one of the main problems in microfinance is methodology. There is great debate about how to measure the impacts of microfinance programs (impact evaluation), especially on human capital, with which we start. We then examine the main findings, drawing heavily on a systematic review by van Rooyen, Stewart and de Wet (2012).

Methodological issues in measuring the impact of microfinance on human development

Most researchers in microfinance start their work by asking how to identify the marginal changes in the lives of microfinance users. Most measure the impact of microfinance by comparing recipients of microfinance with a control group that has no access to microfinance.

In most cases the studies apply nonrandomized approaches. But the choice of the control group was criticized and the economic literature shifted to randomized approaches. These studies use randomized controlled trials (or experiments), in which two groups-the treatment group and the control group-are exactly the same along all relevant dimensions, except access to microfinance. The allocation of individuals in the treatment and control groups is random. Roodman and Morduch (2009) conclude that both randomized and nonrandomized approaches have weaknesses and strengths, and therefore both could be useful when analyzing the impact of microfinance. (Studies using randomized and nonrandomized approaches in fact lead to mixed effects of microfinance on human capital.)

New methodologies are also used. McIntosh, Villaran and Wydick (2011) developed the retrospective analysis of fundamental events contiguous to treatment. According to the authors this allows them to measure welfare changes-due to a treatment such as access to microfinance-based on a single cross-sectional survey in which questions are included on fundamental events in the history of respondents.

Education

A systematic review was made by van Rooyen, Stewart and de Wet (2012) of microfinance impacts on Sub-Saharan Africa. Their search found more than 6,000 studies on the web. They narrowed the sample to 383 "probably relevant" reports. Reading the full texts of these enabled them to identify 69 relevant impact evaluations. They excluded those that did not relate to Sub-Saharan Africa-showing how relevant the methodology is and the need for caution. Limited research has been conducted on the effects of microfinance on education. And wherever studies have been carried out, the results and trends are not always clear. Becchetti and Conzo (2010) found that microfinance generated positive effects on child schooling only when the income of the parents was above a certain threshold. In situations where the increased access to microfinance failed to raise income to a level higher than the threshold at which parents do not send their children to school out of necessity, there was no significant effect. Microfinance effects depended on income and schooling costs.

Augsburg and others (2012) looked at the effects of microfinance on education from the change in the way time was spent after receiving a loan. This was in light of the fact that most microfinance loans went toward financing businesses to be run by the household. They found that in households where the head's highest level of education was primary school level, children between ages 16 and 19 ended up spending more time helping run the family business. In some cases this led to a decrease in school attendance.

In Africa there is an even less systematic evaluation of microfinance impacts on education.

Several studies reported contrasted effects. Studies from Uganda show that participation in microfinance is associated with increased investment in children education. 30 But findings from South Africa provided little evidence for increased enrollment. 31 Beneficial effects may therefore be context specific. For Mosley and Roch (2004), microcredit often enhances human capital through increased expenditures on education and related improvements in health, which may then extend to poor individuals through intra-household and intergenerational effects.

The most robust studies reported in annex 16.1 show that microcredit seems to have a positive impact on education, [START_REF] Adjei | Lacall Calderon and others[END_REF] while others show a negative effect for primary schools. [START_REF] Shimamura | [END_REF] The remaining studies show positive and negative impacts depending on gender or district. It seems difficult to generalize and to argue for a clear effect of microfinance on education in Africa. More research into the implications of microfinance on education is clearly needed to better understand why the theoretical channels mentioned above do not always work in practice. 34

Health

All the studies mentioned in annex 16.1 indicate that microfinance has a strong positive effect on health, whether the study was centered on microcredit only, microsavings only, or both. Microcredit improves the health budget of the household and in some cases awareness of health care of children. In some programs the MFI uses its position to give health information to its clients.

Interventions that improve financial access may complement interventions to improve health conditions. [START_REF] Pronyk | [END_REF] In cases where MFIs deliver health-related services through microcredit groups, for example, there is strong evidence of a positive impact on health. These positive health outcomes are not only general, but include maternal and child health too. [START_REF] Leatherman | [END_REF] In Ghana, MFIs have contributed to both community and national malaria initiatives by increasing the level of knowledge dissemination. 37 In Uganda 32% of women who received education about HIV/AIDS prevention through microcredit groups tried at least one HIV/AIDS prevention practice, versus 18% of non-clients. [START_REF] Barnes | [END_REF] And a positive impact was found in South Africa where comprehensive training and education programs on microfinance group members, for whom the risk of physical or sexual abuse by intimate partners was reduced by more than half compared with a control group of microcredit-only members and with the general public. [START_REF] Pronyk | [END_REF] Another improved dimension is the positive impact of microfinance on nutrition, as seen in longitudinal studies in Ghana. 40 Similar results were found in South Africa. 41 Annex 16.1 also shows that a fair number of the studies have found a positive impact of microfinance on food security. Improving nutrition, especially for children under 5, is vital for their health.

These findings strengthen the social effect of microfinance and are used by those who are in favor of subsidizing MFI. In recent debates, social benefits from improved health for MFI clients can be compensated by subsidies. But recommendations on sustainability of MFIs may lead to less impact on health if the MFI cares more about income than the social benefits (improved health). Further, small loans used for income generation can reduce poverty directly, while simultaneously causing wider benefits, including better health. Access to reliable borrowing and saving mechanisms makes it easier to pay for medicine and clinical visits. 42

Poverty reduction

Several studies have shown the positive impacts of microfinance on poverty reduction in Africa.

Microfinance has been described by Morduch and Haley (2002) as an instrument that, under the right conditions, fits the needs of a broad range of the population. Chipeta and Mkandawire (1991) have shown the positive impacts of microcredit in Malawi. Mosley and Roch (2004) present six case studies in Africa. They examine the indirect effects of microfinance (mainly micro-savings) on the poor as they believe benefits to the poor are more likely to be felt in this way. They conclude that microcredit to the non-poor can reduce poverty by drawing very poor people into the labor market as employees of microfinance clients, and that microcredit enhances social capital and helps the poor build social networks.

Most African poor especially in rural areas have no access to energy services and providing agencies. Rao and others (2009) show how energy microfinance is helpful, arguing that "energy has strong links with poverty reduction through income, health, education, gender and the environment. Without ensuring minimum access to energy services for a broad segment of the population, countries have not been able to move beyond a subsistence economy." These results confirm those of Saghir (2005) who shows that the energy sector has strong links to poverty reduction.

Women's empowerment

Empowerment of women is a complex process of change with power being rooted in social systems and values. The concept of empowering women through microfinance is therefore not a straightforward one with available evidence giving a mixed picture of both success and limitations. Empowerment is often viewed, among other things, as increased participation in decision making, increased political power and rights, and increased self-esteem.

As Cheston and Kuhn (2002) show, microfinance empowers women in several ways. Access to credit and participation in income-generating activities is assumed to strengthen women's bargaining position in the household. There is also evidence of them having an increased decision making role in family planning, education of daughters and other such household matters. Further, through their own businesses set up using microfinance loans, they have decision making power in these businesses.

Women also appreciate the non-income benefits of a group lending program as much as or more than credit. This includes the expanded business and social networks, as well as the increased respect and prestige they receive from both male and female family members and the community at large. Kim and others (2007) also show evidence that economic and social empowerment of women can contribute to reductions in intimate partner violence. But Kabeer (2005) points out that while access to financial services makes important contributions to economic productivity and social well-being of poor women, it does not automatically empower women. Afrane (2002) conducted a study on impact evaluations in Ghana and South Africa that focus on impact results. Microfinance interventions achieved significant improvements in business incomes, access to life-enhancing facilities and empowerment of women.

Impact summary

Microfinance has adverse impacts on human capital in Africa. While its effects on health and women's empowerment are well established, there is a lack of evidence about the impact on education. There is a need to run a systematic impact evaluation of microfinance programs in Africa. The recent debate worldwide about the need to assess MFI interventions only on a financial basis (sustainability) may lead to targeting the middle class and keeping the poor out of the scope.

Moreover, recent studies show adverse effects of microcredit. Crépon and others (2011) report the results of a randomized experiment designed to measure the impact of microcredit in rural areas of Morocco. The experiment was based on the assignation of a large set of villages to treatment villages where Al Amana, the largest MFI in Morocco, offered credit and control villages where no credit was offered. They find that providing access to microcredit did not lead to new business creation, only to an expansion in the scale of existing businesses. Households with no business at the baseline merely increased consumption once they got access to credit. (This again reminds us that credit, by itself, cannot spawn entrepreneurs.)

They also find that treated households decreased their wage employment and increased their consumption of leisure, offsetting the income gains realized from the scale expansion of businesses. If this finding has external validity, it not only casts a shadow on the income generating potential of microcredit, but also raises longer term questions about the borrowers' ability to repay their loans and the possibility of chronic indebtedness.

Policy implications

Despite the debate about the impacts of microfinance on human capital formation, most NGOs and governments advocate its use to empower women and to fight poverty. Following our analysis, in Africa at least three challenges need to be addressed. First, this industry-unlike that in Asia and South America-needs to strengthen its skills. Second, while the industry needs to be sustainable in Africa, its human capital impacts need to be considered by policymakers. Third, there is a need to develop the micro-insurance services in Africa that are particularly needed in areas prone to climate-change effects and to natural disasters.

Building skills

One of the most recurrent issues in this developing industry in Africa is its need of skills to face its challenges. In fact, each country has specific needs. There is a strong need for local skilled workers in this industry to have basic skills but also particular knowledge of the area covered or the population's needs. Most MFIs do not have the ability to train their staff or build the skills locally. This is particularly true for cooperatives, NGOs and small MFIs that face financial constraints. Setting programs and training sessions for microfinance workers in their local context will increase the efficiency and human capital impact of microfinance. This industry needs to work closely with the national education system to develop curricula3 . Each country has its own characteristics and microfinance-industry needs to reach the target population.

Strengthening sustainability of MFIs and targeting the poor

The tradeoff between sustainability and achieving social goals by MFIs is at the center of current debates in microfinance. While Africa is a promising continent for microfinance, generalization of overcautious practices may minimize its human capital impacts and may lessen any benefits to the poorest. Starting from these considerations, the long-run sustainability of microfinance may lead MFIs to target the middle class and the upper fringe of the poor that offers better returns. Sustainability may indeed be achieved by this shift in the target population, but microfinance will become less inclusive. Policymakers need to reverse these trends if the target is financial and social inclusion. Specific actions, programs or institutions are needed for the excluded populations, especially the poorest populations. NGOs are better positioned for this market segment and need to be helped by local bodies. The health and educational dividend for society surpasses the subsidy costs to these NGOs.

Promoting micro-insurance

Micro-insurance in Africa is less developed than micro-saving and microcredit, due to lack of awareness among the poor, financial constraints and absence of insurance promotion by MFIs, as well as economic viability.

Developing micro-insurance is vital for Africa. Severe climate change is provoking migration of the poor from rural areas to towns where they often live in slums. Increasing access to microinsurance may limit these trends. Most rural poor are making their living from agriculture where income is highly variable. Micro-insurance will protect these populations from such swings by pooling and protecting against financial risks. These instruments may increase enrollment of children in school and limit the scope for child labor. The micro-insurance mechanism will act as a safety net allowing the poor to invest more in human capital (health and education) [START_REF] Van Rooyen | synthesis of what works in microfinance in Sub-Saharan Africa and their financial and human capital effects[END_REF] . Promoting micro-insurance needs strong partnerships between public and private sectors.

Conclusions

While there is a well-established consensus about the potential positive impact of microfinance on human development, especially for the poor, voices are also heard about its practical limitations. Negative voices focus particularly on the functioning of MFIs and the microfinance crisis triggered by them in India, Bosnia, Morocco, Pakistan, Nicaragua and Nigeria. MFIs are under tremendous pressure to become self-sustainable. There are also concerns about public health services offered by some of them because MFIs lack expertise in this area and offering such services is cost-ineffective.

While microcredit and micro-savings are developed to a degree in Africa, micro-insurance requires further attention. Africa's poor, like those in the rest of the world, appear to have a strong preference for micro-savings, but we need a more systematic evaluation of human development effects of microfinance programs. The health dividend seems well established, though education outcomes are less observable. But microfinance is still evolving in Africa and its real impact may be felt only once it reaches optimal coverage.

There is also a strong need to better assess evaluation studies by encouraging scholars to set better research settings and to carry more systematic and in-depth studies. Observing the human capital dividends needs long-term studies for a cohort of individuals (a generation at least) to make conclusions on the real impacts of microfinance, including its sociocultural and ethnic dimensions.

Annex-1: Findings on impact of microcredit models on health, food security and nutrition, and education

Figure

  Figure-1: Conceptual framework

29 Box- 1 :

 291 Sources of business financing: If you needed money to start a business, where would you primarily go? Source: Gallup World Poll 2009 and 2010 (https://worldview.gallup.com)The Gallup World Poll asks what sources of financing individuals would consider using to start a new business. The data show that 42.3 percent of all responders in Sub-Saharan Africa state family as the primary source of funds for potential business needs (box-1). Community savings groups, through which groups of people save money together, are a popular mode of savings in parts of Africa and are cited as a source of funds by about 10 percent of those surveyed. Importantly, commercial banks are reported by about 20 percent of responders.Box-2: Awareness of MicrofinanceSource : Gallup World Poll 2009 and 2010 (https://worldview.gallup.com)

Table - 1: Median size of a microloan, by WHO 1 region

 -

		Microloan size per borrower
	WHO region	USD	International $
	Africa	203	779
	Americas	636	1,428
	Eastern Mediterranean	257	674
	Europe	1,254	3,120
	South-East Asia	99	453
	Western Pacific	282	1,073
	Source: Durairaj and others 2010b.

Table - 2: Population covered by micro-insurance in Africa, 2011

 -

	Country	Population coverage (%)
	Namibia	55.8
	South Africa	54.5
	Swaziland	12.0
	Zimbabwe	11.0
	Tanzania	7.3
	Ghana	7.0
	Mauritius	6.1
	Senegal	6.0
	Uganda	4.6
	Kenya	3.2
	Togo	3.1
	Ethiopia	2.6
	Comoros	2.6
	Benin	2.3
	Tunisia	1.9
	Malawi	1.7
	Cameroon	1.7
	Botswana	1.2
	Burundi	1.1
	Source: Making Finance Work for Africa 2013.

Refer Bongjoh, F., Foko, B., Ndem, F., & Gueye, M. (2014), "Making National Education systems work for Africa", in "One Billion People, One Billion Opportunities"Soucat, A., and Mthuli, N. (eds) (Chapter 17), African Development Bank. Tunis.

[START_REF] Bongjoh | Making National Education systems work for Africa[END_REF], "Making National Education systems work for Africa", in "One BillionPeople, One Billion Opportunities" Soucat, A., and Mthuli, N. (eds) (Chapter 17), African Development Bank. Tunis.

[START_REF] Durairaj | Transforming People's Lives in Africa through financial Inclusion in Health" in "One Billion People, One Billion Opportunities[END_REF], "Transforming People's Lives in Africa through financial Inclusion inHealth", in "One Billion People, One Billion Opportunities" Soucat, A., and Mthuli, N. (eds) (Chapter 23), African Development Bank. Tunis.


Clément Conand
email: clement.conand@altran.com

Estelle Randria

Alexandre Le Borgne

Conand Clément

Le Borgne Alexandre

New Environmental Indicators for sustainable cities of varying size scale: The use case of France

Keywords: Environment governance, Cities environment indicators, Sustainable smart cities, Air pollution, Water pollution

published or not. The documents may come     New Environmental Indicators for sustainable cities of varying size scale: The use case of France

Introduction

Many data are collected nowadays about air quality, energy consumption, quality of life… To address environmental issues, their interpretation and the way they are exploited need to be optimized to obtain proper indicators for citizens and city leaders. Those indicators are useful not only to inform the population but also to provide them clues on which directions they need to improve for city development. Those indicators must be accessible and exhaustive. Our objective is to provide a methodology to produce scores with different indicators related to environmental topics, to improve cities development and increase citizens involvement. Ease of use is the core of this methodology: based only on existing and available governmental data and national goals and laws, it is a low-cost solution, and therefore can be available for any city, regardless its size and its state of development.

In order to get citizens involved in environmental change, it is important to provide them not only with detailed and trustworthy information, but also to make them understand the impact of environmental issues on other aspects of their daily life. In fact, giving indicators may prove to be unsuccessful if citizens can't link them to social dimension. The methodology result should be a final score, which could help cities to focus on the main aspects to improve.

State of the art

This study assumes that global warming must be slow down, and citizens want more ecology in their cities. The Intergovernmental Panel on Climate Change (IPCC) reports on climate change 2021 [START_REF] Masson-Delmotte | Summary for policymakers in Climate Change 2021: The Physical Science Basis[END_REF] indicate that GHG (Green House Gases) increased in atmosphere since 1750 and mostly for CO2 (47%), CH4 (156%) and NO2 (23%). This human impact influences global surface temperature (+0.8°C to +1.3°C from 1850-1900 to 2010-201911), land precipitation has likely increased, sea level increased by 3.7 mm/yr between 2006 and 2018. In order to include environment protection int their programs, city leaders have to be familiar with those topics.

Sustainability was defined in the 1987 Brundtland report as "the balance of economic, social and environmental development" [2]. More than 413 indicators already exist to evaluate sustainability [START_REF] Mori | Review of sustainability indices and indicators: Towards a new City Sustainability Index (CSI)[END_REF] and ecological impact [START_REF] Joss | Eco-city indicators: governance challenges[END_REF] of cities. The problem with cities sustainability is the citizens and city leader vision of environmental indicators. They receive wrong information about prices, regulations and controls, and indicators are not sufficiently linked to specific environmental effects [START_REF] Button | City management and urban environmental indicators[END_REF]. Studies suggested environment indicators on many topics like Impervious surface coverage [START_REF] Arnold | Impervious surface coverage: the emergence of a key environmental indicator[END_REF].

These environment indicators must be linked to economical. institutional and social indicators [START_REF] Valentin | A guide to community sustainability indicators[END_REF]. A detailed method to define social indicators for a sustainable smart city was presented in [START_REF] Marsal-Llacuna | City indicators on social sustainability as standardization technologies for smarter (citizen-centered) governance of cities[END_REF].

In France, standards exist for air and water pollution [START_REF]Article R 221-1 du code de l'environnement[END_REF], [START_REF]du Conseil du 16 décembre 2008 établissant des normes de qualité environnementale dans le domaine de l'eau[END_REF], air pollution [START_REF] Lanier | Spatial analysis of environmental inequalities caused by multiple air pollutants: A cumulative impact screening method, applied to the north of France[END_REF], [START_REF] Brousmiche | Data for the assessment of vulnerability and resilience in the field of environmental health in the north of France[END_REF] and insulation of buildings [START_REF]à R 126-29 du code de la construction et de l'habitation[END_REF] for example, but standards and thresholds is not always understandable for citizen and city leaders. A method based on North of France already exists, it is a study case to determine an environmental score [START_REF] Lanier | Spatial analysis of environmental inequalities caused by multiple air pollutants: A cumulative impact screening method, applied to the north of France[END_REF] but it requires data which are difficult to obtain, such as "low level of education" or "no access to car" for the 36 000 French cities. The second point is that this method has been developed for mapping representation and not to determine a distinct score for each city. Therefore, we propose a new approach for measuring different indicators related to different topics linked to environment and make them interpretable for anybody.

Methodology

Based on literature recommendations, a new methodology was built using existing open access data. The case study is the Occitanie region of France, where interactions and feedbacks from city leaders are at the core of the elaboration of our smart city study. We started with cities which air pollution data were available, as it is not the case for many cities in Occitanie region.

Data used to develop this methodology come from open databases, and are mostly provided by the French governmental data platform [START_REF]Accueil -data[END_REF]. All data sources are available in the tab "Data sources" of the Supplementary Materiel 1, which link is in the Acknowledgement. The key points of the methodology are: (1) Available data: they must be easy to find and free; (2) Indicators built from understandable data measures;

(3) Any French city, regardless of its size and number of citizens, can apply this method.

First, we studied raw data to identify trends between environmental data, eco-social data, and size of cities. Second, we proposed various scores for different selected indicators before comparing them. All indicators, data, scores and sources for selected cities are available in the "Cities data and scores" tab of the Supplementary Materiel 1.

For environmental score, indicators were selected from different categories: water contamination, air pollution, habitat insulation, electrical consumption, gas consumption and natural and artificial surfaces. Those categories include different indicators. For example, air pollution is composed of 12 indicators on PM2.5, O3, NO2 and SO2 concentration in the air.

For eco-social conditions, we selected unemployment rate and poverty rate. Finally, cities were classified in three categories: less than 20 000 people (category "Cities 1"), between 20 000 and 100 000 people (category "Cities 2") and more than 100 000 people (category "Cities 3").

Detailed scores are calculated with different methods. For unemployment and poverty, the score is based on unemployment rates (%) and poverty rate (%). This rate is then normalized to 40 % for poverty, meaning that 40 % of poverty gives a score of 0/100 and 0 % of poverty gives a score of 100/100. The 40 % value is defined with the maximum values observed. The same method is used for unemployment but with a normalization of 30 %.

For water and air pollution, scores are determined based on comparison between measured concentrations and standard concentrations. Standard values for air pollution [START_REF]Article R 221-1 du code de l'environnement[END_REF] are 10 µg/m 3 (on average per day) for PM2.5, 120 µg/m 3 for O3, 40 µg/m 3 for NO2 and 50 µg/m 3 for SO2.

For water pollution, we chose many indicators with standard values for most of them [START_REF]du Conseil du 16 décembre 2008 établissant des normes de qualité environnementale dans le domaine de l'eau[END_REF] except for taste and look, total chlorine and revivable air bacteria at 22°C in 68 hours. For taste and look, a "no comment" gives a 100/100 score, and any special observation gives 0/100. For total chlorine, 0 mg/l gives 100/100 and >1 mg/l gives 0/100. The 1 mg/l limit for chlorine is chosen with the maximum concentration observed and is discussed in detail in the discussion part of this publication. For the revivable air bacteria at 22°C in 68 hours, <1 mg/l gives 100/100 when >=1 mg/l gives 0/100. The other scores (bacteria, pH, ammonium and aluminum) are calculated thanks to the standard values, defining the 0/100 scores.

Scores for the EPD (Energetic Performance Diagnostic of buildings) depend on two criteria for which the insulation of buildings show an estimated consumption (from < 51 kWhEP/m².yr to > 450). The second criteria is the estimated GHG emissions (from <5 kgeqCO2/m 2 .yr to > 80).

Electrical and gas consumption scores are all calculated from consumptions in different sectors that are Residential (R), Tertiary (T), Industrial (I), Agricultural (A) and Other (X). Final scores for those aspects are composed of two calculated scores which are the Per capita electric average consumption for R area (standardized) and for all sectors (standardized). Normalization is defined with the maximum observed values.

Finally, the Total land score is composed of two scores that are the rate of artificial soils and the rate of natural soils. The natural soil score is calculated by adding Forest and semi-natural soil rate, Wetland rate, Water surfaces rate and Natural soil rate.

4

Results and environmental scores First, we analyzed data which can help cities for environmental transition. As energy is a very important aspect in environmental transition, we first studied electrical and gas consumption in cities (Fig. 1a and1b). Electrical consumption presents two patterns (Fig. 1a): one showing less than 35 % of Residential (R) consumption and a distribution of the major component between Industrial (I) and Tertiary (T) consumption. The second one is share between R and T consumption, with a majority of R consumption.

For gas consumption (Fig. 1b), each city shows less than 55 % of R contribution, and a majority with less than 30 %. Electrical and gas consumption distribution show the major contribution of non-residential activities in the total consumption.

Land use on cities territory (Fig. 1c) is divided into three categories: artificial, agricultural, and natural. This indicator is important for environmental aspect, but also for risk prevention because land use has major impact on it. Flood risks could increase by 255 % in 2030 [START_REF] Bubeck | Global warming to increase flood risk on European railways[END_REF] and the reduction of artificial land surfaces could reduce this risk. The distribution of values (Fig. 1c) shows a clear distinction between small cities with a distribution mostly between agricultural and natural land, with a majority on agricultural. For medium and big cities, artificial and agricultural components are the highest with a majority of artificial land. Most of those cities are composed of less than 20 % of natural land.

To present the results of social, air pollution, water pollution and electrical consumption, we chose a histogram distribution based on the city size (Fig. 1d). We can observe on those diagrams that unemployment rate, annual NO2 max emission and mean electrical consumption are growing with the number of citizens in a city. However, chlorine in drinkable water is clearly lower for bigger cities (Cities 3).

The second part of this study is about the calculated scores, their analysis and review, and linkage between them.

Table 1 introduces scores that we calculated for every group of cities. As it is shown, we calculated two main scores that are Unemployment-Poverty and Environmental scores. Those scores are calculated as the average of their sub-scores which have not been weighted for our approach. All scores are calculated on a scale of 0 to 100.

Unemployment-Poverty score is calculated from both unemployment and poverty sub-scores and a higher score indicates good social conditions and a lower one poverty and unemployment. After those observations on the different topics, we decided to study correlations between our different scores (Fig. 2) to highlight noticeable relationships between them. As data were originally collected by hand and stored in a datasheet, performing statistics calculation was not possible because of heterogeneity of the original datasheet. We first separated all data by topics or subtopics in different datasheets and ensured homogeneity concerning names and format conventions. Only raw data were extracted, and scores calculation has been automatized with Python code, and then applied to fill our new datasheets. That way, scores can be calculated automatically whenever new data is added and new cities are interesting by this method. Once this step was realized, it was possible to perform correlations. Correlation used in this method was the Pearson correlation, used to determine whether a linear relationship exists between two variables. Concerning EPD scores, we noticed that it has a moderate positive correlation with the unemployment-poverty score. A city with low unemployment and poverty tends to have a better EPD score. An unexpected finding is that the correlation between EPD and electric scores is very low, even though we could expect electric consumption to be strongly related to EPD. A focus on PM2.5 score highlights two interesting correlations: a moderate positive correlation with water quality and a strong positive correlation with the land use score. The first correlation proves that PM2.5 and water quality scores increase in parallel, which means air quality and water quality hold the same priority for cities leaders. However, the correlation between chlorine score and PM2.5 is moderately negative, indicating that the good quality of drinkable water in small cities is probably due to an important chlorine quantity. The second correlation shows that when more natural land is available, PM2.5 score is better, as air is cleaner than in cities. The strongest correlation is between the NO2 score and the number of inhabitants of a city with a R=-0.84: this very strong negative correlation demonstrates that a rise of the population is linked to a decrease in the NO2 score. Two other interesting correlations are the ones between population, land use and electricity scores. The negative moderate correlations show that when population increases, the two scores decrease. To sum up, a growing number of inhabitants tends to be incompatible with an improvement of air quality score, land use score and electricity score, which is consistent with the fact that it is difficult to favor city expansion while improving environmental issues: NO2 production increases, as well as land artificialization and energy consumption.

Discussion and conclusion

In this paper, we proposed a new methodology to calculate an environmental score of cities compared with eco-social score. We built this methodology with the goal to use only open-source accessible data to make it as generic as possible.

Energy consumption analysis of cities shows that tertiary and industrial sectors represent the biggest part of energy, compared with residential consumption. It is an important indicator for cities if they want to reduce consumption. However, scores are even more distant to each other for the residential sector with higher consumption for the bigger cities, showing a clear difference of citizens consumption depending on the size of the city.

Scores computed on 31 cities in the Occitanie region of France show that the increase in citizens number is correlated with an increase in NO2 and PM2.5 air pollution, artificialization of land and electric consumption (Fig. 2). The very low correlation between EPD and electric consumption shows that cities with a good insulation of buildings do not present lower electrical consumption. This is maybe because the higher correlation of unemployment-poverty score is with EPD score, indicating that richer cities have good insulation but high consumption.

Our scores were built considering that each aspect (energy consumption, air pollution, unemployment…) holds an equivalent weight in the final environmental and social scores. But it is possible that each of them may have a different influence and should be weighted differently in our calculation in order to obtain finer-grain results. This solution will be explored in the future.

To discuss standards and limits of pollution and environment, we want to discuss the adherence to current standards and the absence of standards for some of the pollutants. Current standards in air pollution are not met for most pollutants and it is even more true for PM2.5 (standard is 10 µg/m 3 ) with maximum one-day average of 75.6 µg/m 3 and NO2 (standard is 40 µg/m 3 ) with maximum one-day average of 105.4 µg/m 3 . The second observation is that the bigger is the city, the higher is the PM2.5 and mostly NO2 pollution. Finally, there are very few stations measuring SO2 concentration (maximum one-day average of 89 µg/m 3 ) and even fewer measuring H2S concentration (maximum one-day average of 24.6 µg/m 3 ) [START_REF]Atmo Occitanie[END_REF]. The number of stations measuring those sulfur gases should increase for a more detailed analysis. Some pollutants do not have standards in France, and the best example is chlorine in drinkable water. Chlorine used as a disinfectant has impacts on transmission of antibiotic-resistant genes [START_REF] Ghernaout | Is not it time to stop using chlorine for treating water?[END_REF]. Standards indicate impact on human health for concentrations of free chlorine > 2 mg/l [START_REF] Hsu | A carbon nanotube based resettable sensor for measuring free chlorine in drinking water[END_REF]- [START_REF]Disinfectant use in water treatment[END_REF] but the major part of chlorine studies are old and a more recent study suggests a limit of 0.2 mg/l [START_REF] Sikder | Effectiveness of multilevel risk management emergency response activities to ensure free chlorine residual in household drinking water in Southern Syria[END_REF], 10 times lower. A recent detailed study could be interesting to identify potential effects of chlorine on human health in intermediary concentrations (< 2 mg/l).

Data in this study reveal concentrations between 0.03 and 0.78 mg/l with clearly lower concentration for Cities 3 (Fig. 1d). This difference is probably due to a difference of water disinfectant method: medium and small cities do not have the same water treatment plants as the big cities and compensate with additional chlorine.

To discuss actual standards, bacterial standard for drinkable water is set to ≤ 0 n/(100mL). The problem is that detection methodologies do not reach this precision, and most measurements indicate <1 n/(100mL) and the standard should be changed. Now that our methodology is available, we should confront it to more cities from other regions. With more data, we will be able to carry out further studies and confirm whether correlation exists between our scores, because performing correlations now will lead to non-significant results as there are too few data for big cities (Cities 3). Because we need to provide more results for comparison and performance evaluation sake, the future work on this topic will be to collect needed data to calculate those scores on the 36 000 French cities. To assess the relevance of our scores, we will also collect feedback from cities on their usability. To progress further, we are developing an automated solution written in Python language with a web site interface. This tool will allow all citizens and city leaders to see environmental scores on all French cities. The main differences with other existing scores [START_REF] Lanier | Spatial analysis of environmental inequalities caused by multiple air pollutants: A cumulative impact screening method, applied to the north of France[END_REF] is the reproducibility aspect of our method which can be calculated for all French cities, the easy access of the required data and the understandable scores.

With Covid-19 epidemic, lots of CO2 sensors are installed everywhere, and many data will soon be available to study evolution of concentrations of gas in closed spaces. Thus, we could complete our study with an indoor air quality score.

Fig. 1 .

 1 Fig. 1. Available data about a) Electrical consumption distribution in the city: rates of Tertiary (T), Industrial (I) and Residential (R) sectors; b) Gas consumption distribution in the city: rates of T, I and R sectors; c) Land use distribution: rates of artificial, natural, and agricultural lands; d) Histogram distribution depending on the cities size are obtain using pandas and seaborn Python libraries. Indicators are unemployment rate (%), annual NO2 emission comparing to the limit standard of emission (%), total chlorine in the drinkable water (mg/L) and mean consumption per citizen for the Residential sector (Mwh).

Fig. 2 :

 2 Fig. 2 : Pearson correlations between scores. A high absolute value indicates a high correlation when a low absolute value indicates a low correlation. A positive value indicates a positive correlation when a negative value indicates a negative correlation. Green framed values are the most interesting and comment values.

Table 1 .

 1 Scores average, median and standard deviation (SD) for the 3 city groups.Environmental score is calculated on a scale of 0 to 100, and a good score means low pollution, consumption, and natural land. This environmental final score is the average of six intermediate scores which are as follows:• Water quality score evaluates quality of water in cities. Big cities have better scores due to lower concentration of chlorine, aluminum and ammonium and higher number of indicators. • Air pollution score evaluates quality of air in cities. For this score, SO2 is quite difficult to analyze because of a lack of available data. NO2 score is the most discriminating and is often exceeding thresholds as it is the case for PM2.5. • EPD (Energetical Performance Diagnostic) score evaluates energy performance of people housing. This indicator doesn't present any trend at first sight. • Electrical consumption score shows clearly lower scores for bigger cities, and mostly for the Residential sector. • Gas consumption score is difficult to interpret since gas is less used in big cities than in rural cities. • Land use score evaluates the usage of land especially by analyzing how much natural land is available for population. The biggest cities present more artificial lands and thus, lower scores.

	EPD (Energetical Performance Diagnostic); GHG (Green House Gases); consumption R (Residential); con-
		sumption AS (All Sectors)
	City size (1, 2, 3)	Cities 1 (small)		Cities 2 (medium)		Cities 3 (big)
	Statistical indicators	Aver-age	Median SD	Aver-age	Median SD	Aver-age	Median SD
	Unemployment score	55.6	59.3	17.5	38.9	40.3	16.5	30.9 30.0	12.2
	Poverty score	65.5	67.5	14.3	46.5	50.0	15.6	31.3 28.8	13.9
	Total Poverty-unemployment score	59.7	59.3	15.3	42.7	45.2	15.1	31.1 29.4	13.0
	Bacteria in water score	100.0 100.0	0.0	100.0 100.0	0.0	100.0 100.0	0.0
	Water taste and look score	100.0 100.0	0.0	100.0 100.0	0.0	100.0 100.0	0.0
	Chlorine in water score	63.5	72.0	22.0	68.2	66.5	20.5	79.0 77.5	5.9
	Water pH score	67.9	70.0	17.7	55.0	55.0	13.5	63.8 62.5	10.3
	Water ammonium score	100.0 100.0	0.0	100.0 100.0	0.0	100.0 100.0	0.0
	Water aluminum score	78.1	79.5	17.2	75.2	72.5	16.9	82.8 82.8	11.0
	Water Revive 22°C bacteria score	68.8	100.0	47.9	70.0	100.0	48.3	75.0 100.0	50.0
	Number of water indicator score	86.6	85.7	17.1	94.3	100.0	10.0	92.9 92.9	8.2
	Total water quality score	82.2	84.0	9.2	83.3	86.6	8.0	86.7 89.2	7.5
	PM2.5 air pollution score	41.5	40.2	7.7	34.4	24.6	19.1	32.3 33.7	4.8
	O3 air pollution score	70.2	70.1	3.2	70.0	69.3	4.6	71.9 71.4	1.3
	NO2 air pollution score	91.6	93.1	7.5	82.3	80.3	4.6	53.9 53.6	9.3
	SO2 air pollution score	89.9	89.9	12.5
	Total air pollution score	70.3	69.6	13.1	71.1	75.7	10.4	52.7 53.7	4.7
	EPD GHG score	58.0	74.7	28.9	49.1	45.6	13.7	58.5 58.7	4.2
	EPD consumption score	42.8	56.6	27.6	34.6	33.1	13.9	45.6 46.4	5.9
	Total EPD Score	50.4	66.7	28.0	41.8	38.8	13.3	52.1 52.4	4.5
	Electrical consumption R score	78.1	83.7	18.7	51.7	54.7	23.5	31.4 30.3	28.1
	Electrical consumption AS score	72.4	79.7	27.3	56.6	63.9	22.5	45.3 53.8	25.3
	Total electrical consumption score	75.2	78.1	19.2	54.1	58.0	19.0	38.3 43.2	25.5
	Artificial land score	83.2	85.8	11.7	55.9	66.0	25.3	34.5 31.7	24.9
	Natural land score	23.8	23.0	20.3	10.2	6.5	11.7	10.5 2.9	17.0
	Total land use score	53.5	52.9	11.8	33.0	36.9	16.5	22.5 16.2	19.4
	Gas consumption R score	62.6	93.9	47.6	90.8	95.1	12.6	98.1 98.1	2.4
	Gas consumption AS score	66.4	97.3	45.3	97.5	97.3	2.3	99.3 99.3	0.4
	Total gas consumption score	64.5	95.6	46.3	94.2	96.2	7.4	98.7 98.7	1.4
	Total environmental score	66.4	67.2	5.9	62.4	63.1	4.9	54.7 54.1	7.0

Acknowledgment

The authors acknowledge the financial support by the Digital Factory (Altran part of Capgemini) and we greatly appreciated the assistance of Martine Aubret and David Barret. The Supplementary Materials 1 is shared and made freely available on the open-access repository Mendeley Data (doi: 10.17632/j66797tdw9.2 and link: https://data.mendeley.com/datasets/j66797tdw9/draft?a=e3608fb5-fe27-4e07-b9e8-afeb90488b11).