as mentioned in Chapter 4. Therefore, since their level depends on annual gold production, these values do not change very much for all the legal TMS (i.e. TMS-A to D). However, the difference is drastically noticeable when it comes to the illegal scenario, since i-ASM are supposed not to create wealth through tax payments and, thus, the “royalties” indicator is equal to 0.

<table><caption>Table 29 Consequences assessed for each TMS according to the indicators presented (Table 27) and the thresholds classes (Table 28)</caption><thead><tr><th rowspan="2">Consequences</th><th rowspan="2">Unit</th><th colspan="5">Values</th></tr><tr><th>TMS-A<br>(1 LSM)</th><th>TMS-B<br>(6 MSMc)</th><th>TMS-C<br>(33 ASM)</th><th>TMS-D<br>(1 MSMc + 4<br>MSMg + 20 ASM)</th><th>TMS-illegal<br>(758 i-ASM)</th></tr></thead><tbody><tr><td>Gold production</td><td>kg Au</td><td>5,000</td><td>4,800</td><td>4,950</td><td>5,000</td><td>5002.8</td></tr><tr><td>Direct jobs opportunities</td><td>n</td><td>500</td><td>1,200</td><td>495</td><td>800</td><td>7,580</td></tr><tr><td>Expansion of social infrastructures</td><td>€</td><td>4,187,050</td><td>4,019,568</td><td>4,145,179.5</td><td>4,187,050</td><td>0</td></tr><tr><td>Support of local education, research and skill development</td><td>€</td><td>4,187,050</td><td>4019568</td><td>4,145,179.5</td><td>4,187,050</td><td>0</td></tr><tr><td>Landscape degradation</td><td>ha</td><td>550</td><td>1,800</td><td>3,300</td><td>3,500</td><td>15,160</td></tr></tbody></table>

<table><caption>Table 30 Conversion of the values of the indicators assessed in Table 29 for each TMS in classes based on the defined thresholds (Table 28)</caption><thead><tr><th rowspan="2">Consequences</th><th colspan="5">Risk class</th></tr><tr><th>TMS-A<br>(1 LSM)</th><th>TMS-B<br>(6 MSMc)</th><th>TMS-C<br>(33 ASM)</th><th>TMS-D<br>(1 MSMc + 4 MSMg + 20 ASM)</th><th>TMS-illegal<br>(758 i-ASM)</th></tr></thead><tbody><tr><td>Gold production</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td></tr><tr><td>Direct jobs opportunities</td><td>4</td><td>5</td><td>3</td><td>4</td><td>5</td></tr><tr><td>Expansion of social infrastructures</td><td>3</td><td>3</td><td>3</td><td>3</td><td>1</td></tr><tr><td>Support of local education, research and skill development</td><td>3</td><td>3</td><td>3</td><td>3</td><td>1</td></tr><tr><td>Landscape degradation</td><td>3</td><td>4</td><td>4</td><td>4</td><td>5</td></tr></tbody></table>
## 2. Aggregation of the risks and final score for each territorial mining scenario (TMS)

The final score of a given TMS is obtained through the combination of all the consequences previously evaluated, along with their probability. Data aggregation can be performed through different methods, as mentioned in Chapter 2. Fig. 72 presents the aggregation steps we followed in order to obtain a final RSo score. At this stage, the consequences are not weighted, and they are aggregated by calculating their geometric mean.

Two aggregation steps are considered. Firstly, the aggregation of all the consequences of each type is used to obtain a total negative and positive consequence scores (Fig. 72). In the current RSo, for example, this step is not required for the negative consequences as only one negative consequence has been selected. The second aggregation step, consists in the combination of the total positive score and the total negative score to obtain the global value of the RSo. This is done after the re-classification and conversion of the classes from negative to positive scores to obtain the global value of the RSo the previous Table 22 and Equation 1 (Fig. 72).

<figure><img src="image_74.png" /><figcaption>Figure 72 Conceptual simplified framework of the aggregation method used to combine the different positive and negative consequences in order to obtain a final global score for the RSo of each TMS</figcaption></figure>

### 2.1. Aggregation of the positive and negative consequences

As mentioned, the negative consequences of the current RSo do not need to be aggregated, since only one is considered: landscape degradation. On the other hand, the positive consequences have been aggregated using their geometric mean. Table 31 gives the total final score for each type of consequence. Each considered TMS has a total positive and a negative scores resulting from the geometric mean of their consequences classes. As mentioned, in order to obtain a final RSo score, the total negative scores are converted based on the same rule presented in the Eq.1. and on Table 22 in Chapter 7. The last line of Table 31 presents the converted negative classes.
<table><caption>Table 31 Assessment of the final positive and negative consequences' scores based on the geometric mean of each consequence and converted total negative score based on Table 22.</caption><thead><tr><th rowspan="3">Consequences</th><th colspan="5">Values per Territorial Mining Scenarios (TMS)</th></tr><tr><th>TMS-A</th><th>TMS-B</th><th>TMS-C</th><th>TMS-D</th><th>TMS-illegal</th></tr><tr><th>(1 LSM)</th><th>(6 MSMc)</th><th>(33 ASM)</th><th>(1 MSMc + 4<br>MSMg + 20 ASM)</th><th>(758 i-ASM)</th></tr></thead><tbody><tr><td>Gold production</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td></tr><tr><td>Direct jobs opportunities</td><td>4</td><td>5</td><td>3</td><td>4</td><td>5</td></tr><tr><td>Expansion of social<br>infrastructures</td><td>3</td><td>3</td><td>3</td><td>3</td><td>1</td></tr><tr><td>Support of local education,<br>research and skill<br>development</td><td>3</td><td>3</td><td>3</td><td>3</td><td>1</td></tr><tr><td>Total positive score</td><td>3.5</td><td>3.7</td><td>3.2</td><td>3.5</td><td>2.1</td></tr><tr><td>Landscape degradation</td><td>3</td><td>4</td><td>4</td><td>4</td><td>5</td></tr><tr><td>Total negative score</td><td>3</td><td>4</td><td>4</td><td>4</td><td>5</td></tr><tr><td>Converted total negative<br>score</td><td>3</td><td>2</td><td>2</td><td>2</td><td>1</td></tr></tbody></table>

## 2.2. Aggregation of positive and negative consequences and final score

Once the total negative and positive scores are calculated, a global score can be determined for the entire RSo, for each considered TMS. The combination of the total negative and positive scores is based on the geometric mean (Fig. 72). The result is then multiplied by the probability of occurrence of such consequences (see Table 24). Since the RSo concerns the functioning of the mine site in ordinary and normal conditions, the probability of occurrence of its consequences is assumed to be certain (i.e. "1") (see Table 23). The global score of the RSo for each TMS is given in Table 32. As for the RSa, the higher the global RSo score, the better is the corresponding TMS in ordinary functioning conditions
<table><caption>Table 32 Assessment of the final global score of the RSo for each TMS based on the geometric mean of the total positive and negative consequences and the probability of occurrence. Since the assessment concerns the RSo, probability is fixed at 1 (i.e. "certain")</caption><thead><tr><th rowspan="3">Consequences code</th><th colspan="5">Values per Territorial Mining Scenarios (TMS)</th></tr><tr><th>TMS-A</th><th>TMS-B</th><th>TMS-C</th><th>TMS-D</th><th>TMS-illegal</th></tr><tr><th>(1 LSM)</th><th>(6 MSMc)</th><th>(33 ASM)</th><th>(1 MSMc + 4 MSMg + 20 ASM)</th><th>(758 i-ASM)</th></tr></thead><tbody><tr><th>Total positive score</th><td>3.5</td><td>3.7</td><td>3.2</td><td>3.5</td><td>2.1</td></tr><tr><th>Total converted negative score</th><td>3.0</td><td>2.0</td><td>2.0</td><td>2.0</td><td>1.0</td></tr><tr><th>Total consequences score</th><td>3.2</td><td>2.7</td><td>2.5</td><td>2.6</td><td>1.5</td></tr><tr><th>Probability of occurrence</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><th>Global RSo score</th><td>3.2</td><td>2.7</td><td>2.5</td><td>2.6</td><td>1.5</td></tr></tbody></table>

## Conclusion

The purpose of this chapter was to include the risk assessment of the TMS during the normal operating conditions of the mine. Despite it is not the main focus in this thesis, a simple risk scenario has been developed based on few positive and negative risks. The levels of these risks have been assessed and combined to obtain a final score for each TMS (Table 32).

As shown in Table 32, the illegal-TMS presents the lower global score. Indeed, although this scenario presents the best jobs opportunities, emphasizing the role of illegal gold mining as a social phenomenon driving informal jobs opportunities, the low score is due to the absence of formal payments of royalties filling the public budget and to the considerable surfaces consumed. The latter explication is aggravated by the fact that illegal gold miners do not respect the mining perimeters of the concessions and the environmental protection measures imposed by the law. On the other hand, the best score is performed by the TMS-A. This is explained by the fact that this scenario involves only one large-scale gold mine (LSM). Therefore, at the end, despite the higher surface consumed per-project by a LSM compared to the other gold mine types, this is still less significant than the cumulative surfaces consumed on a scenario-basis with multiple smaller projects (TMS-B, TMS-C, TMS-D), while having almost the same values of positive consequences. Finally, the scenarios TMS-B, TMS-C and TMS-D presents approximately the same average global scores. This can be mainly explained by the fact that the score highly depends on the chosen indicators and their thresholds. Moreover, the lower variability is due as well by the fact that some parameters related to MSMc and MSMg-types – respectively present in TMS-B and TMS-C – are the same (e.g. soil footprint of the mine site). Therefore, a better and more accurate global assessment should integrate more detailed and variable data concerning these projects.
These results depend significantly on the choices made during the assessment, the selected actors, the aggregation methods and the defined thresholds. For instance, although it is less sensitive to different scales and only applicable to positive numbers, the geometric mean is preferred when synthesizing judgment for decision-making purposes (Krejčí and Stoklasa, 2018; Doré-Ossipyan and Sareh, 2020). Indeed, the geometric mean is directly based on all the observations and usually the presence of a few extremely small or large values has no considerable effect on the result.

As mentioned, the RSo in this study needs to be furtherly detailed and deepen, also through the integration of territorial vulnerability which may vary according to project localization. The gaps related to the RSo assessment in this study will be furtherly discussed in Chapters 10 and 11. Here, it is important to point out that the assessment does not account for some specificities, such as the identification of one or more impacted areas (e.g. the mine site perimeter or a wider area within Mana river basin) and the territorial vulnerability of these areas. Also, the aggregation method does not consider the number of studied consequences. Therefore, the combination of five positive consequences and – only – one negative consequence is not normalized, misleading the results. Even if the negative consequence had the lowest value, this would be compensated by the presence of 5 positive impacts. As discussed in Part IV, more complex aggregation methods should be proposed, such as the AHP, in order to evaluate the final score.

Despite such gaps, the presented assessment allows to evaluate the second risk score for each TMS. Hence, in the next Chapter, both the RSa and the RSo scores will be combined in order to obtain a final global score for each TMS. This allows the comparison of the different territorial scenarios and their weighting based on the perspective of the different stakeholders.
# Chapter 9. Global risk score of the territorial mining scenarios and multi-actor weighting

This chapter presents the last steps of our application of our approach. At this stage, for each territorial mining scenario (TMS), the corresponding RSa (Chapter 7) and RSo (Chapter 8) scores have been assessed independently. The risk scenarios integrate social, environmental and technical aspects, and they have been assessed by following an “expertise” approach that requires only limited data.

For a given TMS, once the risk scores of the RSa and RSo are obtained, it is possible to combine them in order to obtain a global risk score for the given TMS. This facilitates the comparison between the different TMS, enhancing the analysis of potential strategies for land-planning in mining regions. Finally, since our approach aims to the involvement of stakeholders during the whole application, a questionnaire survey has been carried out in FG to develop coefficients in order to weight the final TMS scores according to stakeholders perception.

## 1. Assessment of the global risk score of each TMS

### 1.1 Aggregation of RSo and RSa scores

Two risk scenarios have been considered for each TMS: the RSa, which has been the main focus of this thesis (Chapter 7), and the RSo, which has been only introduced here (Chapter 8). The assessment of their risk scores has the objective to finally obtain an global score for each TMS, in order to compare the various TMS for operational purposes to support land-planning decision-making. Fig. 73 summarizes the steps followed until now to get to the global score for each TMS. As mentioned in the previous chapters, the aggregation methods used for this thesis must be furtherly improved but they were chosen to enhance the rapid and simple demonstrative application of the approach proposed in this study.

### 1.2 Global scores of the Territorial Mining Scenarios

The global score of each TMS is presented in Table 33. Each score is the result of the geometric mean of their corresponding RSo and RSa scores. This seemed the aggregation method the most suited in this case. For instance, selecting the maximal value would have reduced the importance of the RSo within the final TMS score. Indeed, RSo scores are globally lower than RSa because of the choices made during RSo assessment. This is due particularly to the stringent thresholds defined for the RSo that results in consequences scores whose global average does not exceed a medium score (~3) with a standard deviation of 1.4 (Table 28). Hence, further studies need to focus exclusively on the normal functioning of gold mine types in FG in order to improve the assessment of the RSo. Further points of discussion are detailed in Chapter 10.
Except for the TMS based only on illegal mines, which displays the lowest global score, the other TMS, which rely on legal mining, present very similar scores. The “best” scenario is represented by the TMS-B, mainly because it seems to assure – according to the data, thresholds and the assumptions used for this assessment – important employment rates with lower surface impacts (in the RSo) and smaller flooded areas in case of dam failure (in the RSa). However, these results must be taken carefully and cannot be fully considered for operational purposes without quantifying their levels of uncertainty. Furthermore, the current methodology does not integrate the use of cyanidation and the chemical nature of the tailings which would potentially alter the final scores of TMS-A and TMS-B. A better estimation of the impact on surface waters – for instance, using water turbidity as indicator – due to the distribution of a great number of ASM (i.e. TMS-C), might also worsen significantly the scores of TMS-C and TMS-illegal. All these considerations are furtherly detailed in Chapter 10.

<figure><img src="image_75.png" /><figcaption>Figure 73 Conceptual flowsheet of the aggregation methods used in this study and the integration of weighting coefficients to the RS scores</figcaption></figure>
<table><caption>Table 33 Global risk scores of each TMS</caption><thead><tr><th rowspan="2">Territory objectives<br>Annual gold production (kg/year)</th><th colspan="7">Territorial Mining Scenario (TMS)</th><th colspan="2">Risk Scenario scores</th><th rowspan="2">TMS Global score</th></tr><tr><th>Scenario Code</th><th>IAS M</th><th>IAS M</th><th>AS M</th><th>MSM g</th><th>MSM c</th><th>LS M</th><th>RSo</th><th>RSa</th></tr></thead><tbody><tr><td rowspan="4">5000,00</td><td>TMS-A</td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>3.2</td><td>3.4</td><td>3.3</td></tr><tr><td>TMS-B</td><td></td><td></td><td></td><td></td><td></td><td>6</td><td>2.7</td><td>4.2</td><td>3.4</td></tr><tr><td>TMS-C</td><td></td><td></td><td>33</td><td></td><td></td><td></td><td>2.5</td><td>4.2</td><td>3.2</td></tr><tr><td>TMS-D</td><td></td><td>20</td><td>4</td><td>1</td><td></td><td></td><td>2.6</td><td>3.9</td><td>3.2</td></tr><tr><td></td><td>TMS-illegal</td><td>758</td><td></td><td></td><td></td><td></td><td></td><td>1.5</td><td>3.4</td><td>2.3</td></tr></tbody></table>

## 2. Multi-actor assessment of the relative importance of each consequence

As mentioned in Chapter 2, our approach aims to involve stakeholders all along its application, starting with the definition of the territorial objectives and the development of the TMS. Therefore, a crucial step of our approach is the weighting of the global TMS risk scores according to stakeholders' perspective. The weighting is based on the interests and the perceptions of the stakeholders considered in this study (e.g. civil society, public administration and gold mining operators) (see Table 5). More precisely, this means to evaluate the importance that each stakeholder group gives to the socio-ecological assets affected by the risks considered in the RSo (Table 16) and the RSa (Table 17).

In this section, a quick introduction aims to emphasize the utilization of a questionnaire survey for the purposes of this study and to point out the method used to develop, distribute and analyze the questionnaire in this study. Finally, the results are shown, and the weighting coefficients are estimated and applied to the RSo and RSa scores previously assessed.

### 2.1 Questionnaire used to evaluate stakeholders risk perception

Among all survey methods, questionnaires are commonly used for risk analysis for data gathering, stakeholder's definition, assessment, preferences or behavioral analysis and risk weighting (Carpino et al., 2019; Robinson and Botzen, 2019; Zhuang et al., 2019). For the purposes of the current application, a simple questionnaire has been developed based on existing studies not related to the mining sector (El-Sayegh, 2008; Rosenthal, 2016) and distributed among local stakeholders. The method used for the development and the use of the questionnaire (Appendix 15) is shown in Fig. 74 and furtherly detailed below.

We have developed a semi-structured questionnaire, divided in two sections. The first section is intended to gather information about respondents' profile. Six general questions
address general information of each respondent (e.g. age, birth and residence place, professional activity). The answers are anonymous, thus, no information concerning the identification of the respondent is demanded.

The second section is intended to get the perception of the *relative importance* that each respondent gives to the corresponding socio-ecological assets that are potentially impacted by gold mines. The assets concerned by the questionnaire correspond to the list of consequences listed in tables 16 and 17, respectively for the RSo and RSa assessment. To do so, 13 questions aim to obtain a score regarding the importance that the respondent attributes to a given asset (e.g. drinking water, public infrastructures, population safety and health, biodiversity), where 0 is “not important” and 4 is “very important”. Each respondent gives the score to each socio-ecological asset according to its perceived functionality, need or use. This step allows to apprehend the relative importance of that asset in the case it was positively or negatively impacted by one/multiple gold mining activities. For example, one of the questions concerns which level of importance (between 0 and 4) the respondent gives to the preservation of the physical status of surface waters, against riverbank erosion or the diversion of streams. The response gives an idea of the importance that is given to this asset if a gold mine would affect it.

Finally, few open questions concern the respondents' identification of supplementary non-cited assets potentially impacted by gold mining – in order to enrich the risk identification database presented in Chapter 6 – but also information regarding how respondents received the questionnaire and their willingness to obtain the results of the survey. A copy of the questionnaire is available in Appendix 15.

The questionnaire has been distributed both directly and indirectly to people through personal interactions during the last mission in FG (January 2020), email distribution and local intermediaries from the University, public administrations, NGOs and mining operators all around the region. Concerning Mana river basin, the questionnaire has been personally distributed directly to the main municipal services and shared to Mana inhabitants.

<figure><img src="image_76.png" /><figcaption>Figure 74 Simplified method used to develop weighting coefficients to apply to each TMS score</figcaption></figure>
## 2.2 Results of the questionnaire and actor-based weighting coefficients

At this stage, 39 questionnaires were returned. However, only 34 questionnaires were completed and susceptible to be furtherly analyzed to estimate the weighting scores. As mentioned, each question was answered by the respondents giving a score between 0 and 4. These scores were averaged for each asset and then normalized in order to obtain weighting coefficients between 0 and 1, where 0 represents the lowest relative importance of the respondent to the corresponding asset and 1 the highest one. The coefficient normalization is based on the following simple rule presented by Eq.2.

$$W = \frac{v-min}{max-min} \qquad \text{Equation 2}$$

Where:

*W*, is the normalized weighting coefficient between 0 and 1 and it can be based on all the collected answers (*W*<sub>gen</sub>) or distinguished according to the actor-category of the respondents, whether they belong to civil society (*W*<sub>cs</sub>) or public administration (*W*<sub>pa</sub>);

*v*, is the score given by each respondent on a scale from 0 to 4;

*min*, is the minimum value of the non-normalized score (i.e. in this case, 0);

*max*, is the maximal value of the non-normalized score (i.e. in this case, 4)

While *W*<sub>gen</sub> refers to the weighting coefficients related to the totality of the respondents, few values have been extrapolated distinguishing the different stakeholders groups, only to give a demonstrative example of a multi-perspective assessment related to society (*W*<sub>cs</sub>) and public administration (*W*<sub>pa</sub>) (Table 34). The stakeholder's category "mining operators" could not be considered since only one respondent belonged to this category.
<table><caption>Table 34 Socio-ecological assets evaluated in the questionnaire survey and the corresponding RSo and RSa consequences code. On the right columns, the weighting coefficients derived from all the complete answers (W<sub>gen</sub>), or from the respondents belonging to the "civil society" (W<sub>cs</sub>) and "public administration" (W<sub>pa</sub>) categories</caption><thead><tr><th rowspan="2">Assets</th><th colspan="2">Corresponding consequences</th><th colspan="3">Weighting coefficients<br>(0 - 1) (W)</th></tr><tr><th>RSa</th><th>RSo</th><th>W<sub>gen</sub></th><th>W<sub>cs</sub></th><th>W<sub>pa</sub></th></tr></thead><tbody><tr><td>Non drinkable water</td><td>Reduction of surface water ecosystem services</td><td>-</td><td>0.81</td><td>0.76</td><td>0.88</td></tr><tr><td>Drinkable water</td><td>Reduction of drinking water public provisioning service;<br>Reduction of drinking water; provisioning service (other than public);</td><td>-</td><td>0.95</td><td>0.93</td><td>0.96</td></tr><tr><td>Water physical quality</td><td>Reduction of surface water ecosystem services</td><td>-</td><td>0.79</td><td>0.79</td><td>0.84</td></tr><tr><td>Mining infrastructures</td><td>Destruction of the dam</td><td>-</td><td>0.40</td><td>0.32</td><td>0.48</td></tr><tr><td>Public roads</td><td>Degradation of public infrastructures</td><td>Expansion of social infrastructures</td><td>0.69</td><td>0.64</td><td>0.75</td></tr><tr><td>Public infrastructures</td><td>Degradation of public infrastructures</td><td>Expansion of social infrastructures</td><td>0.69</td><td>0.67</td><td>0.73</td></tr><tr><td>Biodiversity</td><td>Reduction of biodiversity-related ecosystem services;<br>Reduction of forest-related ecosystem services</td><td>-</td><td>0.88</td><td>0.89</td><td>0.86</td></tr><tr><td>Soil erosion</td><td>Run-off and soil degradation</td><td>-</td><td>0.78</td><td>0.78</td><td>0.77</td></tr><tr><td>Soil fertility</td><td>Reduction of crop production service</td><td>-</td><td>0.79</td><td>0.78</td><td>0.82</td></tr><tr><td>Air quality</td><td>-</td><td>-</td><td>0.85</td><td>0.88</td><td>0.82</td></tr><tr><td>Mining job opportunities</td><td>-</td><td>Direct jobs</td><td>0.57</td><td>0.55</td><td>0.59</td></tr><tr><td>Public health</td><td>Direct impact on local population;<br>Health Impacts</td><td>-</td><td>0.88</td><td>0.87</td><td>0.88</td></tr><tr><td>Miners' health</td><td>Direct death and injuries of workforce;<br>Reduction of forest-related ecosystem services;</td><td>-</td><td>0.69</td><td>0.68</td><td>0.66</td></tr><tr><td>Landscape quality</td><td>Reduction of esthetic ecosystem services;<br>Impacted surface</td><td>Landscape degradation</td><td>0.87</td><td>0.89</td><td>0.84</td></tr><tr><td>Economic outcomes</td><td>-</td><td>Raw material supply;<br>Direct jobs;<br>Support of local education</td><td>0.43</td><td>0.43</td><td>0.38</td></tr></tbody></table>

Table 34 shows the normalized weighting coefficients deduced from the survey, for each socio-ecological asset (first column). The second and third columns represent the corresponding consequences, related to the RSo (Table 16) or RSa (Table 17), to which a given weighting coefficient is applied. The weighting coefficients used in this study are those in the W<sub>gen</sub> column and they are related to the global results from all the average of all respondents.
Although insufficient data do not allow an actor-based distinction, an example of multi-perspective weighting coefficients is just presented in the last two columns (*Wcs* and *Wpa*) for civil society and public administration. However, these coefficients will not be applied since, not enough data have been gathered from the current survey to capture the weight variability of each actor category.

At this stage, the highest weighting coefficients are generally given mostly to ecological assets (e.g. soil fertility, air quality, landscape quality) with a specific peak to water resources for both its social and ecological functions and to public health. Assets related to gold mining activities have the lowest values here (e.g. mining infrastructures, mining job opportunities, miner's health) according to both civil society and public administration. The economic outcomes from mining activities represent the lowest peak of the presented coefficients.

## 2.4 Global TMS risk scores after risk weighting

Once the weighting coefficients are calculated (Table 34), they are applied to the scores of each risk scenario as shown in Fig. 73. Each weighting coefficient is applied to the corresponding consequence in the risk scenario, as presented in Table 34. Table 35 shows the application of the general weighting coefficients to obtain weighted RSa (Table 35a) and RSo (Table 35b) scores and finally the global TMS scores (Table 35c).

The application of the coefficients does not alter significantly the original ranking of the non-weighted TMS. According to this demonstrative application, the potentially TMS with the highest score responding to the objective of a total annual gold production at 5,000 kg at the Mana river basin scale are the TMS-B, which involves six MSMc and TMS-A, with one LSM. Then, 33 ASM (TMS-C) and the mixed scenario (TMS-D) present the same scores. Moreover, the worst RSa score is obtained by the TMS-A, mainly because of the score given to the "surface area" component (i.e. the flooded area), which is the largest one in the TMS-A. However, the reduced surface of only one mine site, used as indicator for "landscape degradation" in the RSo tends to increase its position in the final TMS scores ranking. Finally, the illegal scenario obtains the worst scores which is due especially to its very low RSo score and to the fact that the probability score applied is equal to 1, since no safety measures are assumed implemented by illegal miners. However, the difference between the scores of the legal TMS are very low and the assessment must be integrated with uncertainty analysis to fully apprehend which might be the most suitable alternative for a land-planning strategy.

It has been mentioned already in the previous section that the scoring does not take into account the cumulative impacted areas. For instance, the length of impacted surface waters could be more important in TMS-C and TMS-illegal. These scenarios involve smaller mines but in a much higher number. This higher distribution of mining activities compared to the other TMS must be quantified more precisely in its cumulative dimension. Moreover, the products used for mining processing and the chemical nature of the tailings must be integrated as well, since they would considerably alter the current scores for all the TMS, including the final score of TMS-B.
<table><caption>Table 35 Final TMS scores with the application of the general weighting coefficients</caption><thead><tr><th>Territorial Mining Scenarios scores with W<sub>gen</sub></th><th>TMS-A</th><th>TMS-B</th><th>TMS-C</th><th>TMS-D</th><th>TMS-illegal</th></tr></thead><tbody><tr><td><strong>Weighted RSa score (W<sub>gen</sub>)</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Impacted surface score</td><td>5</td><td>3</td><td>2</td><td>3</td><td>3</td></tr><tr><td>Weighted impacted surface score</td><td>4.34</td><td>2.6</td><td>1.74</td><td>2.6</td><td>2.6</td></tr><tr><td>Consequences weighted score</td><td>2.88</td><td>1.98</td><td>2.33</td><td>2.33</td><td>2.33</td></tr><tr><td>a) Consequences weighted score with probability</td><td>1.44</td><td>0.99</td><td>1.75</td><td>1.75</td><td>2.33</td></tr><tr><td>Total weighted score</td><td>2.5</td><td>1</td><td>0.8</td><td>1.2</td><td>1.2</td></tr><tr><td>Total weighted score with probability</td><td>1.2</td><td>0.5</td><td>0.6</td><td>0.9</td><td>1.2</td></tr><tr><td><em>Conversion</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total converted weighted score</td><td>2.5</td><td>4</td><td>4.2</td><td>3.8</td><td>3.8</td></tr><tr><td>Total converted weighted score with probability</td><td>3.8</td><td>4.5</td><td>4.4</td><td>4.1</td><td>3.8</td></tr><tr><td><strong>Weighted RSo score (W<sub>gen</sub>)</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>b) Total weighted positive</td><td>1.81</td><td>1.91</td><td>1.68</td><td>1.81</td><td>1.10</td></tr><tr><td>Total weighted negative</td><td>2.60</td><td>1.74</td><td>1.74</td><td>1.74</td><td>0.87</td></tr><tr><td>Total weighted score</td><td>2.17</td><td>1.82</td><td>1.71</td><td>1.77</td><td>0.98</td></tr><tr><td><em>Global weighted TMS score (W<sub>gen</sub>)</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>c) Global non-weighted score (with probability)</td><td>3.3</td><td>3.4</td><td>3.2</td><td>3.2</td><td>2.3</td></tr><tr><td><strong>Global weighted TMS score (with probability)</strong></td><td><strong>2.9</strong></td><td><strong>2.9</strong></td><td><strong>2.7</strong></td><td><strong>2.7</strong></td><td><strong>1.9</strong></td></tr></tbody></table>

Conclusion

The current chapter presents the final steps of the demonstrative application of the approach proposed in this thesis. After the RSa and RSo are respectively assessed in Chapters 7 and 8, the final scores for each TMS are here estimated and weighted based on stakeholder's perception.

The multiple limitations encountered during this test-application (e.g. lack of data and assessment methods, threshold references) are furtherly discussed in Part IV (Chapters 10 and 11). Nevertheless, despite the high level of uncertainty that must be quantified in future studies, our approach was fully applied, through a rapid and simple assessment based on the information currently available.

Therefore, further studies should ameliorate the current application not only integrating new data but also developing and applying more solid methods for risk assessment and for uncertainty analysis. In this way, the comparison between the TMS might have a higher reliability. Furthermore, the integration of existing decision-making tools (e.g. TOPSIS, AHP) could improve considerably the operationalization of our approach and support public authorities, civil society but also mining operators for the definition of land-planning strategies.
# Part IV – Discussion of the approach proposed in this study and its application
This thesis proposes an original approach to integrate mining into land-planning strategies through risk assessment at the territory level. This approach has been operationalized through the development of a methodology (Part I) via an application-test on gold mining in FG (Parts II and III). The current thesis shows the feasibility of the proposed approach and the achievement of many of the objectives presented in Chapter 2 (e.g. the integration of territorial and technical-operational features of mining activities, stakeholders' involvement). The first goal of this thesis is to show what can be accomplished through a territorial and pluridisciplinary approach, that includes things usually treated differently and separately. Nevertheless, Despite the feasibility of the methodology and, hence, of the proposed approach which allows a better view of mining projects at wider scales, this approach and its application present some important points of discussion. These aspects will be considered in this last part of the thesis. Discussion concerns both the case-study that has been the object of this thesis (e.g. the choices made, the data used) (Chapter 10) and the general philosophy behind our approach (e.g. the adaptability to other cases, the improvement of its operationalization) (Chapter 11).

# Chapter 10. Discussion on the application of our approach to the French Guiana gold mining sector

Chapter 10 discusses the application of our approach to gold mining in FG. More precisely, it addresses the analysis of the results of what has been realized in Part II, also pointing out the gaps and the potential improvements.

## 1. The influence of system definition on the final scores

In Chapters 3 and 4, the systems to which our approach is applied have been presented and defined. For a more practical development and comparison of the territorial mining scenarios (TMS) a multicriteria classification of gold mines in FG has been proposed (Scammacca et al., 2020) in Chapter 4. Section 1 of Chapter 10 discusses the representativity or the limits of the territory and mining systems chosen in this study and how these choices influence the results.

### 1.1. *Mana river basin: a representative territorial unit of French Guiana?*

The territory system considered for this study is the Mana river basin. The choice of the river basin as territorial unit and the specific selection of Mana river basin have been justified in Chapter 4, according to the geology of the basin (especially the distribution of gold deposits), the types of gold mines, as well as the presence of human settlements and environmentally sensitive areas. For its features, Mana river basin seems a representative sample to describe and analyze gold mines in FG, their risks and the average level of socio-ecological vulnerability.
As described in Chapter 4, common parameters and vulnerability factors existing for all the other basins in FG are found in Mana river basin. The representativeness of the studied area is important because it allows to have an idea about the generalization of the application and the adaptability of our approach. This would have been biased if the territory system chosen would have presented socio-ecological features that were not found elsewhere in FG.

If the methodology is extendible to other river basins in FG, further applications should account for peculiar characteristics that deserve more precise assessments. For instance, if the Kaw river basin was chosen (Fig. 17), the unique ecological vulnerability of this area should have been considered in risk analysis, weighting, mine site localization. Kaw hosts an important protected wetland reserve but also a significant gold deposit that has been targeted by different projects. In 2007, the large-scale gold mining project of Camp Caiman, proposed by the Canadian company Iamgold has been the object of important concerns. Finally, the project has been suspended by the French Government for environmental concerns and to underline its engagement towards the key-regulations of French political Agenda (i.e. *Grenelle de l'Environnement*).

Also, if the Maroni or the Oyapock river basins were selected, the application should have necessarily included the description and assessment of gold mining activities respectively in Suriname or in Brazil, the related regulatory frameworks and their risks. This also would have implied the gathering of data in different countries. Also, in the latter case, another parameter should have been considered. These two river basins hosts a large part of the Guiana Amazonian Park, where multiple sites of Native Americans are also located. Together with the Tumucumaque National Park in neighbouring Brazil, the Guiana Amazonian Park represents the biggest rain forest protected area in the world. These factors would significantly increase the socio-ecological vulnerability of the area and the related data would be difficult to be detailed.

To support the choice of representative basins and their features, detailed hydrological modeling of water catchment systems in FG and their specificities could be performed. A comparison tool might be coupling the ONF catalog (ONF, 2015) – which gives spatialized information about the biophysical assets of forest ecosystems in FG (e.g. soils, biodiversity quality, type of vegetation, landscape quality) – and the delimitations of the hydrographic sectors representing the river basins.

## 1.2. The variability of gold mine types in FG and the limits of their classification

### 1.2.1. Gold mine-types vs real mining projects

Our study focuses on standardized mine types that have been developed via a multi-criteria classification of existing or potentially existing mining projects in FG. The classification has been detailed in Chapter 4. The replacement of real gold mining projects and their specific characteristics by standardized prototypes represents a methodological choice also to
emphasize the operationalization of an approach that can be performed also for potential future strategies and other TMS than the ones selected in this thesis. Furthermore, this choice was driven by the current concerns regarding real projects that might have limited the process of data gathering and risk assessment. The existence of a range of characteristics for each project type is a useful tool for the acknowledgement and the evaluation of risks related to each gold-mine type. Furthermore, the development of such classification allowed to structure the gold mining sector in FG, with the further possibility, as it will be discussed in the following sub-sections, to include new gold mine types that might – or might not – see the light in the region.

### 1.2.2. The variability of mining projects and the normalization of the gold mine-types

The classification of gold mines given in Chapter 4 is based on multiple criteria (e.g. geological, juridical, technical) and it is presented along with an extract of the main characteristics of each project type. These criteria manage to comprehensively include the existing gold mines in FG. However, few considerations should be added. First of all, the “soil footprint” criterion varies according to the employed mining techniques. For this reason, it is rather a parameter rather than a distinguishing criterion. For instance, no project in FG were currently performing large-scale underground mining at the moment the classification was being developed. According to some recent prospections, the new mining company Orea – which includes the *Montagne d'Or* project – is considering underground techniques for the project *Maripa Gold* shared with Iamgold near the Kaw wetland (Orea, 2020). Therefore, these mine-types should be included in the classification. A gold mine classified under the LSM-type using underground techniques would have a lower soil footprint than a MSM-type operating surface mining techniques. Also, underground mining might help reducing the potential volume of mine wastes. Therefore, at this stage the “soil footprint” criterion might help to distinguish more importantly MSMg from MSMc and MSMc from LSM-types.

Therefore, the development of TMS varies according to the actual evolutions of mining projects and their techniques but also on where the assessment is performed. For instance, we said that if the Maroni river basin was chosen, the classification should have focus as well on gold mining in Suriname. A mining technique currently used in Suriname or in Guyana is gold dredging along the Maroni river (Kioe-A-Sen, et al., 2016, Hook, 2019). This technique involves dredges that extracts gold from sand, gravel, and dirt using water and mechanical methods and it would represent a supplementary specific gold mine-type with specific risks. Therefore, in the case of application on the Maroni river basin, these activities should be identified and detailed and can be integrated on updated classifications of gold mine-types.

Hence, the identification and comparison of risks specific to each project type should be based on a priori normalization of such risks to the specific features of the project, which influence risk nature and intensity. The normalization allows to obtain pertinent ratios between
the features of the gold mine-types and the risks that such activities generate. Table 36 lists some examples of information that can be derived from Tables 9-11. For instance, the ratio between annual extracted gold and the surface footprint of each project type might be a global socio-environmental indicator to compare positive economic production outcomes and negative environmental consequences of a mining activity before any further detailed assessment. Since they use cyanidation for gold recovery, MSMc and LSM seem to assure a better ratio but only because the normalization did not consider the potential impacts related to this process. The inclusion of water consumption might add as well considerable information about the overall environmental performance of a project. The ratio between direct employment and soil footprint provides further information about the comparison of the positive outcomes of mining, in terms of employment, to the loss of surface due to the development of the mine. For instance, the difference between MSMg and MSMc is mainly due to the fact that the construction and the operation of the cyanidation plant involve a higher number of specialized workers and technicians. The ratio between the maximal dam height and the annual extracted gold might be as well an indicator comparing the potential impact of a dam failure indicated only by the height of the dam, and the annual gold production. However, the use of the dam height as simple indicator might be not pertinent. As already explained, if higher and larger dams' failures generate much more considerable impacts than smaller dams, they might be also the less likely to fail because of specific monitoring programs that could be more effective in large mines, because of higher investments and training. On the other hand, the content of the tailings pond and possible chemical additives (e.g. cyanide, thiosulfates) should be considered, for instance modeling the potential interactions between these compounds and the environment (e.g. chemical speciation) or even through the less detailed assignment of expert-based-scores to the corresponding gold mine-types.

<table><caption>Table 36 Examples of information that can be derived for each project-type from Tables 9-11</caption><thead><tr><th rowspan="2">Examples of indirect deducible information (per-project)</th><th colspan="2">i-ASM</th><th rowspan="2">ASM</th><th colspan="2">MSM</th><th rowspan="2">LSM</th></tr><tr><th>i-ASMp</th><th>i-ASMs</th><th>MSMg</th><th>MSMc</th></tr></thead><tbody><tr><td>Annual extracted gold/Mine soil footprint ratio (kg/ha per year)</td><td>5 - 10</td><td></td><td>~ 1.5</td><td>~ 1.5</td><td>~ 3</td><td>5 - 10</td></tr><tr><td>Total direct jobs/Mine soil footprint ratio (number of workers/ha)</td><td>5 - 10</td><td></td><td>~0.15</td><td>~ 0.3 - 0.6</td><td>~ 0.4 - 0.7</td><td>0.7 - 1</td></tr><tr><td>Total direct jobs/Annual gold production ratio (worker/kg per year)</td><td>1</td><td></td><td>0.1</td><td>0.3</td><td>~0.7</td><td>~1.4</td></tr><tr><td>Maximal dam height/Annual extracted gold ratio (m/kg per year)</td><td>n/a</td><td>n/a</td><td>0.02</td><td>0.07</td><td>~0.04</td><td>~0.01</td></tr><tr><td>Water consumption/Annual extracted gold ratio (m3/kg per year)</td><td>n/a</td><td>n/a</td><td>n/a</td><td>n/a</td><td>~6</td><td>~12</td></tr></tbody></table>
### 1.2.3. Difficulties of a project(s)-based approach for illegal mining (i-ASM)

The illegal gold mining sector deserves a peculiar discussion compared to the other gold mine-types. The first question concerns a terminological issue. As discussed in Chapter 4, no unanimous definition of illegal gold mining is given. Furthermore, a neat distinction must be made between illegal and informal mining. For instance, Nhlengetwa, (2016) distinguished between “illegal invasive mining” which occurs “when miners illegally enter the old mine workings of decommissioned mines” and “informal mining”, which is a “community-based mining that typically follows customary law”. These differences are often not considered by mining policies, but they do need to be addressed, especially in risk assessment and land-planning processes. This should be implemented in the approach applied in this study, in order to optimize and precise the classification of gold mine-types, through the gathering of more detailed data from local authorities that help distinguishing, for instance, two additional sub-types i-ASM (e.g. illegal vs informal gold mining) and their specific socio-technical features.

Indeed, another question concerns the quantity and quality of information related to illegal gold mining. Firstly, the difficulty in defining this type of mining activity relies on the fact that they are extremely heterogeneous. Also, because of the volatility of such activities, i-ASM are difficult to track and data are extremely variable and hardly available. Annual production rates of illegal gold mining highly differ from one country to another (Seccatore et al., 2014), and from site to site. Secondly, the notions of project or mine site are extremely fuzzy for the i-ASM sector. Therefore, the integration within a project-type approach of a type of mining activities which represents rather a “social phenomenon” – where it is difficult to determine where a mine site ends and another one begin – must include the support of social and human sciences.

The information presented in Table 11 (Chapter 4) is based essentially on the global consideration of mining activities cumulatively on a sector-basis, rather than individually on a project-basis. The values shown are based on the estimation of a total annual impacted surface of 1,000 – 2,000 hectares (Rahm, 2017), a total annual produced gold reaching 10 tons and a total number of illegal miners which may reach 10,000 people (WWF, 2018). These data are effectively considered in the study but, based on what previously explained, their uncertainty might be even higher compared to legal projects and, therefore, must be quantified.

### 1.3. Adaptability of the proposed classification to other case-studies

The use of standardized mine types is an original approach that serves two purposes: i) the comparison of potential future scenarios of mining development and ii) the analysis of such scenarios and the related risks at the territory level. Therefore, this approach might be adaptable to other studies concerning other mining and territory systems. However, the substantial results of the classification – thus, the standard mining project types presented here for the gold mining sector in FG – can be only partly adapted to other cases. First of all, the
partial adaptation concerns the exploitation of the same commodity (i.e. gold), since each
commodity involves different geological, geographical – and thus, political and socio-ecological
– but also technical features.

1.3.1. Adaptation to the studied commodity: is gold a commodity like others?

As for their impacts, mine types depend also on the type of targeted commodity. “Annual productivity” is a criterion that significantly depends on the commodity and its market. For instance mining production of bauxite is not comparable to gold production rates. Nevertheless, these differences can easily be adapted within the classification. However, the exploitation of bauxite will not require the same infrastructures and techniques used in gold mines. For instance, the amounts of ore to transport can be much higher, which can play an important role for risk assessment.

Also, as mentioned, the i-ASM sector is a social phenomenon peculiar to gold and other few commodities (e.g. cobalt, diamonds and other gems) (Hentschel et al., 2002). Moreover, since it is poverty-driven, it concerns often countries with particular socio-economic and political backgrounds (the so called “developing” countries). The difficulty in defining this type of mining activity relies on the fact that, as previously mentioned, they are highly heterogeneous and that data are usually unavailable.

Furthermore, some specifities of the gold commodity should be tackled since its main
sectors of demand are jewelry, investment and banking, with very low industrial implications
(World Gold Council): gold could be considered as a strategic product rather than a simple
mineral resource. Therefore, it may not be a representative commodity for a demonstrative
study. Moreover, the necessity of gold mining and its applications might be discussed in relation
to its socio-environmental costs, while gold recycling rate is approximately equivalent to about
87% of reported consumption (USGS, 2020). The application of our approach to industrial
commodities, for instance, could list, among the positive risks to assess, the benefits generated
from the mining activity to respond to local or worldwide technological needs.

1.3.2. The variability of gold mining in different “territorial system(s)”

Even when the targeted mineral commodity in two different countries is the same, the
classification is affected by the territory system where mining is performed. The particularities
of each territorial system, givin a same commodity, concern a great range of aspects. For
instance, the nature of the regulatory framework of a State plays a an important role. For
example, gold mining sector in Suriname is very similar to FG. Since they both are located within
the Guiana Shield, the types of geological deposits are almost the same. However, Surinamese
laws do not forbid the use of Hg-gold amalgam for gold recovery. Thus, the use of Hg-based
recovery methods cannot be used as a distinction criterion between legal and illegal gold mines
in Suriname. Furthermore, it has been said that gold dredging along the Maroni river is a mining
technique currently used in Suriname, representing a supplementary gold mine-type to add.

In the case of gold mining, another specificity concerns the medium-scale mine-type (MSM). MSM represents a middle type between ASM and LSM that is specific to FG and very few countries, such as Brazil. In the Guiana Shield and in the nearby areas (e.g. Bolivia, Peru), similar features are displayed by “mining cooperatives” but they represent rather the entrepreneurial and institutional choice of a structured sector for small and medium-scale miners to reduce negative socio-environmental costs and maximize economic performances (Alves et al., 2019).

Finally, political and regulatory challenges might affect the pertinence and the
effectiveness of such a classification for risk assessment purposes. These aspects are related
to:

- the permeability between legal and illegal gold mines in some areas of the world;

- situations where political decisions might take over the technical dimension of a mining project becoming the main final driver for its feasibility or suspension;

- the transboundary nature of the consequences generated by a mining project.

The first two points are extremely related to the capacity and transparency of
governmental authorities in regulating their mining sector and the related decisions.
Furthermore, the linkage between legal and illegal economic activities is hardly quantifiable in
our approach but it must be considered when it comes to decision-making and stakeholder's
concertation. Concerning the latter point, in some areas multiple project types with contrasting
regulatory frameworks might coexist. As mentioned, one of the clearest examples concerns
actually FG with the Maroni river basin, shared with Suriname (Fig. 17). Maroni river basin is
the largest in FG and here two legal frameworks for gold mining coexist in such area: on one
side, legal gold mines using Hg-based recovery methods, and on the other, a Hg-free legal
sector. These circumstances, when not properly addressed, might hinder the risk assessment
process – and generally speaking land-planning strategies – requiring cross-border efforts for
mining governance and policies. Also, FG is the destination of most of the migration flows
present in the Guyana Shield. This is a phenomenon independent from the typology of gold
mine since it is strictly correlated to the global trends of gold and local political and juridical
frameworks at wider scales (Taubira Delannon, 2000).

Therefore, an extensive and detailed analysis of the territory system and the features
of its mining sector must be acknowledged during the application our approach. Since the
particularities of each territory, as it is described below, are hard to quantify, they must be
considered in advance, for the classification of mine-types and the development of TMS but
particularly afterwards, when it comes to decision-making.
## 1.4. *Mining phasing vs territorial variability*

The temporal boundaries chosen for the application of our approach have an important influence on the development of the TMS and the final results. As mentioned, two temporal dimensions should be considered, whether they are related to the mining system or the territorial system. The first one is related to the mining phase (i.e. prospecting, planning, operation, closure) during which the assessment is focused. The second one concerns the variability in time of the parameters of the territory in which mining is performed.

This study focuses on the operation phase. Therefore, the temporal scale here considered only refers to the mining system, neglecting the changes over time of the socio-ecological characteristics of the territory in which mining is performed. These temporal dimensions should be enriched and integrated for instance, through the development of hypothetical and fictional scenarios in which the parameters of the territory and mining systems might change (e.g. new localizations of mining projects, the utilization of new mining techniques, urban intensification (e.g. 2020-2050), new environmental protection measures, the effect of climate change). For instance, on a regional scale, we can consider a first scenario where a mine is located in a catchment with no urbanization. Hence, population vulnerability, which influences the consequences of the risk event, is very low. A second scenario where urbanization is spreading and human clusters are formed in the same catchment, implies the increase of the level of risk. Such issues are vital to develop territory-based risk assessment approaches especially in FG where urbanization is expected to reach approximately 574 000 inhabitants in 2040 (Léon, 2010) (which is almost twice of the current population). Another fundamental issue is related to the impacts of climate change over the next 100 years on the socio-ecological vulnerability of FG and also how these changes might interact with gold mining activities, resulting, potentially, in the aggravation of the negative consequences (Lecomte *et al.*, 2011).

# 2. The influence of the chosen territorial mining scenarios on the results

We compared different territorial mining scenarios (TMS) on the basis of their risk scores. Therefore, how these scenarios have been developed and the risk events have been selected must be discussed to understand the final results of this application-test.

## 2.1 *Territorial objectives and stakeholder involvement*

Except the choice of Mana river basin as territory system and the classification of gold mines, that have been previously discussed, another main step having an important influence on the TMS is the definition of objectives at the territory level. As shown, the TMS are developed based on the combination of one or multiple mine types that, with their characteristics, could meet specified pre-defined objectives. In this study, three examples have
been given in Chapter 5, based on gold production, employment related to the gold mining sector and the limitation of deforested areas. These objectives can be an interesting complementary tool to compare the TMS before the risk assessment. Table 37 shows the different TMS developed in this study – based on the objective of gold annual production equivalent to 5,000 kg – and the assessment of their risk scores. If we look at the other two objectives mentioned in Chapter 5 which haven't been used in this study, for each TMS it is possible to estimate the corresponding values (according to the number and type of projects). This can be, before the detailed assessment, a preliminary tool to discuss and compare the TMS. Indeed, the TMS are ranked in a different order whether we consider their global scores or their employment/soil footprint ratio. This means that the “best” strategy cannot be evaluated only on some given territorial objectives but on multiple criteria which include these objectives as well as the generated and suffered risks by the mining projects (e.g. opportunities and losses).

<table><caption>Table 37 Risk scores and global scores of the TMS developed in this study. If the other two objectives are considered (see Chapter 5) and their ratio is given, the TMS are ranked in a different order</caption><thead><tr><th rowspan="2">Territory objectives</th><th rowspan="2">Territorial Mining Scenario (TMS)</th><th colspan="2">Risk Scenario scores</th><th rowspan="2">TMS Global score</th><th colspan="2">Other territory objectives considered</th><th rowspan="2">Ratio DJ/SF</th></tr><tr><th>Scenario Code</th><th>RSo</th><th>RSa</th><th>Direct jobs (DJ)</th><th>Soil footprint (ha) (SF)</th></tr></thead><tbody><tr><td rowspan="5">5,000</td><td>TMS-A</td><td></td><td>3.2</td><td>3.4</td><td>3.3</td><td>500</td><td>550</td><td>0.9</td></tr><tr><td>TMS-B</td><td></td><td>2.7</td><td>4.2</td><td>3.4</td><td>1,250</td><td>1,875</td><td>0.66</td></tr><tr><td>TMS-C</td><td></td><td>2.5</td><td>4.2</td><td>3.2</td><td>500</td><td>3,333</td><td>0.15</td></tr><tr><td>TMS-D</td><td></td><td>2.6</td><td>3.9</td><td>3.2</td><td>800</td><td>3,500</td><td>0.22</td></tr><tr><td>TMS-illegal</td><td></td><td>1.5</td><td>3.4</td><td>2.3</td><td>7,580</td><td>15,160</td><td>0.5</td></tr></tbody></table>

However, these objectives – which have been defined arbitrarily for demonstrative purposes – should be: compared with stakeholders' perspectives and detailed to see if they actually meet pertinently the real concerns related to gold mining in FG, and more precisely, within Mana river basin. Surveys and participative methods should target local authorities and civil society in order to identify and quantify more accurately a set of objectives based on which the TMS can be developed. Social sciences should be integrated through specific methodologies that help quantifying the involvement of local actors. Statistical analysis and data gathering should focus on the representiveness of the territorial objectives defined in this study, to see if they properly describe the reality. For instance, is annual gold production an objective that all the local actors would actually consider for the development of gold mining sector in FG? Some actors might give more importance to specific objectives rather than others. Local population and public authorities might weight higher the rate of employment and of
reduced deforested surfaces than the produced gold per year. At the same time, for mining operators, annual gold production could be the main objective to consider when it comes to plan strategies for mining development.

The definition of the objectives must be coherent with the spatial boundaries chosen for the application of the methodology. For instance, in the presented study, the territorial objectives concern only and exclusively Mana river basin. Further studies about the interactions among nesting territorial systems (e.g. from the river basin to the region) should be carried out and see, for instance, how this might influence the definition of the objectives and the governance related to gold mining, from the municipality to the central services of the government. For instance, different territorial objectives might be chosen by each municipality, according to their characteristics and goals. For instance, at the scale of Mana river basin the main territorial objective would be a high employment rate while at the Kaw river basin scale, the deforested surface would be a priority. Therefore, the application of our approach must not exclude the possibility of different and new territorial objectives for different territory systems and their spatial scales.

Finally, the current application is based only on the first objective (i.e. annual gold production of 5,000 kg) on which five TMS are built. However, this is rather the case when it comes to concretely purpose decision-making strategies, since public policies and objectives are built on multiple criteria of interest. Therefore, the TMS should be built on multiple objectives at the same time, based on the results of stakeholder's concertation and participative surveys especially if our approach aims to more accurate assessment and more effective decision-making support in land-planning processes.

## 2.2. The TMS: number and types of mining projects

The TMS took a spatialized form through the fictional localization of gold mine types within the Mana river basin. The number of projects depend on the territorial objectives but also on the intrinsic properties (e.g. mining techniques, workforce, mine site surface) for each mine-type, as defined in Chapter 4. However, the important amount of collected data concerning each mine type is dynamic and should be updated based on the changes that might affect the gold mining sector in FG. As we mentioned, recently, a new gold mining project suggested the use of underground mining techniques (Orea, 2020). Secondly, these parameters are expressed in a range of values (Appendix 7). Therefore, it is important to select a given value in order to allow a quantified assessment of the score of the TMS. The choice of a specific value within the range for each parameter of the gold mine-type should be guided by local experts and mining operators and public authorities to avoid under or overestimation of the consequent levels of risk. For instance, the illegal-TMS involves 758 illegal mines against the TMS-C with only 33 ASM. This is explained because, according to the data gathered and interpreted so far, one ASM produces approximatively 100 kg of gold per year, while one i-ASM only 6,6 kg. However, in reality the same miners in one i-ASM-type might work on multiple
projects. The higher gold production of the illegal sector is due significantly to the significant
quantity of illegal mines that cover the territory. This size-vs-quantity issue, which will be
detailed in the next section, is peculiar to the illegal sector – but not only – and its specificities,
if compared to legal projects. Therefore, the assessment of the illegal-TMS does not allow the
proper comparison between the legal and illegal sectors using the same common parameters.
The data concerning illegal projects should be furtherly assessed and detailed, especially if
specific strategies aim to address the role of this sectors into land-planning.

## 2.3. Fictional vs real localization of the gold mines

A final concern is related to the method used for the localization of the gold mines for
each scenario. In this study, as we focused on gold mine-types instead of real projects, we used
fictional localizations for the mining projects. Furthermore, the utilization of fictional
localizations serves the purpose to develop potential future scenarios at the territory level,
although more factors should be considered (e.g. road accessibility, electric generators
nearby). This approach might be used as well by local authorities to hypothesize fictional land-
planning strategies on the long term. Nevertheless, the approach proposed in this study can
focus as well on the real localization of existing mining projects still under development. This
would allow the analysis the mining projects that would see the light on the short term and a
rapid comparison that might support decision-making for immediate operational purposes.

Concerning the fictional localization, since the purpose of this study is not to develop a
method to optimize the siting of gold mines, a simple and basic methodology has been
presented in Chapter 5 based on geological, juridical and socio-ecological criteria and on few
assumptions (e.g. the localization of the project within a 2 to 5 km wide buffer around the
localization of the geological deposit). However, despite its reliability, the localization of the
mine site does influence the level of risk and, thus, the final score of each TMS. Hence, mining
siting should be performed using well more developed methodologies or, when these are not
available, on a higher number of reliable criteria. For instance, Amoah and Stemn, (2018) used
a spatialized multi-criteria analysis for the localization of centralized processing centres for
artisanal miners through an approach similar to ours but using more criteria (e.g. slope and
nearness to other ASM sites, to roads, railways, settlement, tectonic zones, waterbodies).
A recent research program called APPUI-Mines is being realized by the French Geological
Survey (BRGM) that aims to the development of a GIS-based “favorability” methodology for the
localization of small-scale gold mines in FG based on a significantly higher number of criteria
(e.g. protected areas, terrain data, regulations). Once finalized, this could be integrated within
the current application of our approach.
### 3. Rapidity and reliability of the RSa and RSo scores assessment

Our approach allows a rapid and easy comparison of different TMS using existing data, based on the risk scores. This study focuses mainly on the accidental risk scenario (RSa), more specifically in the case of a tailings dam failure generated by overtopping. However, in order to obtain a global TMS score, the ordinary risk scenario (RSo) has been only explored with a simple series of risks. Despite the demonstrative purposes and the transposability of the approach, the results are naturally by the choices and assumptions made during the assessment and their uncertainty still remains an important gap to fulfil. Nevertheless, these choices answers to the fact that this study aims to show the feasibility and the rapidity of our approach, especially in a context where data are lacking.

This is particularly true for the RSa which has been the main focus in this thesis. The central event of the RSa is the tailings dam failure generated by overtopping. The assessment of the RSa score involved a series of estimations, concerning the impacted area (i.e. the flooded area), socio-ecological vulnerability and probability assessment. The following sub-sections detail specific issues related to the assessment of the RSa score.

#### 3.1. Flood area estimations: limits and perspectives

The assessment of the consequences related to the selected RSa was performed, firstly, estimating the directly impacted area by the dam failure by overtopping. As it is shown in Appendix 11, a great range of hydrological, hydrodynamic, hydraulic and rheological models is currently available and applied in different studies, for geotechnical monitoring, flood management and land-planning. However, because of their reliable and detailed prediction, these models are very time-consuming and requiring a considerable amount of data.

Therefore, the method proposed by Concha Larrauri, (2017) has been selected for a first estimation of the flooded areas due to a dam failure since it has been developed specifically for tailings dam failures, when few data are available. Also because of these advantages, this method presents a series of limitations and a rough estimatead flooded area.

Nevertheless, this method is simple, rapid to perform and it integrates few parameters related to the mining project and the structure of the dam. Furthermore, it allows a first operational estimation of the dam failure at the territory level and performed on multiple mining projects at the same time, which makes it adapted to the purposes of this study.

However, from a methodological point of view, this method is built on historical failures related to large-scale mining operation. Therefore, it might be inconsistent for the application on smaller mining projects (e.g. i-ASM, ASM, MSM). Furthermore, the resulting area is more or less represented by a buffer zone around the localization of the gold mines, without considering the downstream direction of the flood and sometimes considerably overestimating the flooded area (Table 38).
For this reason, the method has been integrated with another methodology developed directly on Qgis and presented in Chapter 7, Section 3.3. This second method is only based on DEM and the localization of the mine site. Although the delimitation of the flooded area is more precise than the first method, its accuracy of prediction is very limited to the few data used for the assessment. No considerations are given concerning the level of the water inside the dam, flow velocity, the nature of tailings and other parameters concerning the dam structure or the surrounding environment (e.g. soils permeability, presence of natural or anthropic barriers) (Table 38). Nevertheless, this approach is based on the same theory behind the methodologies usually applied to derive the hydrological network from DEM data. The method is based on the choice of a threshold value of the GIS module used (i.e. Maximal Flow Path Length). The numerical outputs of this module have been interpreted and selected on a subjective observational basis. This represents as well the main grey area of this method, since the value chosen varies on a case-to-case basis and on expert-based opinion, which considerably influence the extension of the flooded area. Therefore, the uncertainty of this second method should be furtherly quantified. As for the first method (Concha Larrauri, 2017), the second method is not intended to substitute more detailed and accurate methodologies for dam failure flood assessment. Finally, the combination of the two methods is realized through limiting the accumulation zones resulting from the second method to the maximal flood perimeter obtained from the first method (Concha Larrauri et al., 2017) (see Fig. 58).

This coupled simple approach has been tested on the Brumadinho dam failure, to see if the flooded area corresponds to the estimated one. It has been shown that, as mentioned, the choice of the threshold values used in the QGis module vary on a case-to-case basis. Furthermore, the maximal run-out distance of the flood is highly overestimated. Hence, it is possible that the estimated flooded areas are much greater than if they were estimated by more accurate models and using more data. The reduction of the flooded area would increase – in positive – the final score of the TMS. But it is still to discuss if such increase would operate proportionally for each TMS or only for the TMS-A, which involves only one large scale mining project and for which the maximal flooded perimeter is considerably wide.

Finally, concerning the integration of much more detailed data, a specific issue is related to fully apprehend the mechanisms of failure, which influence the quality and the quantity of the tailings release. In the case of overtopping, if the dam does not collapse, not all water comes out, and in some cases, it might not exceed the mine site. In the current study, overtopping is used as a triggering mechanism of a dam failure where the total water volume is assumed released, which is rarely the case during overotpping. In the current case, the only adaptation to failure generated by overtopping is given during the application of the first method used, where the total elevation of the dam parameter does not take into account the freeboard (i.e. the difference in elevation between the crest of the dam and the maximum water surface in the reservoir), as it would be the case during an overtopping (where the freeboard becomes equal to zero).
<table><caption>Table 38 Limits and advantages of the methods used in this demonstrative application for the estimation of the flooded area</caption><thead><tr><th>Methods used</th><th>Advantages</th><th>Limitations</th></tr></thead><tbody><tr><td rowspan="6">Concha Larrauri, (2017)</td><td>Specific to tailings dam failures</td><td>Built based on historical tailings dam failures related to large-scale mining only<br>Does not consider flow velocity, route and direction</td></tr><tr><td>Same geological context of FG</td><td>DEM as only external parameter</td></tr><tr><td>Limited input data</td><td>Does not consider nature of tailings</td></tr><tr><td>Based on historical tailings dam failures</td><td>Does not consider variation of water level<br>Based exclusively on large-scale mine failures</td></tr><tr><td>Can be partly adapted to the type of failure</td><td>Does not consider safety measures at the dam level<br>Does not distinguish downstream and upstream areas</td></tr><tr><td>Good rendering at large spatial scales</td><td>Does not consider potential surrounding sink or vectors (e.g. depressions, streams)</td></tr><tr><td rowspan="2">Flow accumulation area method</td><td>Based only on DEM and the localization of the mining project</td><td>Does not consider water level or flow velocity, type of failure, nature of tailings etc.</td></tr><tr><td>Considers the downstream area and the main preferential direction of the flood</td><td>Expert-based selection of the thresholds for the FLP module on GIS-tools<br>Does not consider safety measures at the dam level</td></tr></tbody></table>

## 3.2. Estimation of territorial vulnerability

Once the flooded area has been estimated, vulnerability indicators are assessed, mapped and combined to obtain an overall vulnerability map of territory (Fig. 67). Socio-ecological vulnerability of Mana river basin is independent from the mine-type in the flooded area since it is related to the intrinsic characteristic of the territory system.

The indicators and the methods used to quantify it have been specifically selected and developed for FG based on the available data through an expert-based approach. However, in order to be more solid, the assessment should be integrated with more reliable data and commonly recognized and validated methodologies for land-planning purposes. For instance, soil fertility could be assessed through further specific indicators (e.g. C/N ratio, cation-exchange capacity, total organic carbon) widely used in scientific literature (Drobnik et al., 2018; Yageta et al., 2019). Moreover, because of the territorial resolution of the assessment, some information gathered at local scales can be lost. For instance, multiple aspects of the assessment of soil-related ecosystem services can be discussed since the wide resolution of the assessment does not consider the great horizontal and vertical variability of soils in FG. Hydromorphic soils nearby surface waters which can be greatly affected by changes in hydrological regimes are not considered. Secondly, *oxisols* and *ultisols* present a good permeability (see Appendix 12) only because the assessment focuses on the features of topsoil while deep drainage can often be stopped by clayey layers present within the subsoil. Finally,
speaking of soil fertility, it would be important to assess soil agronomic potential also based on
information related to currently used or potential agricultural practices.

Concerning the aggregation of the scores, the main reasons behind the final outputs
given in Chapter 7 consist in the fact that, firstly, the scores have not been weighted (Chapter
9) at this stage. Secondly, the processing on Qgis of the vulnerability assessment involves a
great range of spatial data only in the form of thousands of polygons and eleven indicators. The
horizontal resolution of the results (i.e. the multiple polygons displayed) involves areas where
the values of the geometric mean are at the maximum or minimum. However, these areas are
“lost” within the great number of polygons (Fig.67a). This is due to the difference of spatial
scales at which the original data are displayed. For instance, the spatial data provided by ONF,
(2015) (e.g. biodiversity vulnerability, wood production, landscape vulnerability) have a higher
resolution compared to the data used for the other indicators. Therefore, here the aggregation
method chosen doesn’t seem able to consider the difference of the resolution at which the
data used for the assessment were originally produced. Secondly, the vertical resolution
concerns the number of vulnerability indicators, thus the number of vulnerability maps that
need to be aggregated. Despite they are a bit more than a dozen, for every pixel there is at least
one indicator that has a high vulnerability value (i.e. 4 or 5), which explains why the aggregation
method by maximum value displays an elevated vulnerability for the whole basin (Fig. 67b). For
the mentioned reason, it is vital to choose or developed other aggregation methods which tend
to consider these two types of resolution and thus, that manage to normalize the final global
score, taking into account the number of indicators considered and the number of data used
for the aggregation. For instance, it could be investigated mono or multi-criteria decision-
making methods for data combination.

Despite the methodological issues mentioned, specific considerations should focus on
the data currently available used for the estimation of each indicator. This kind of information
has been gathered after free-interviews and observations in the municipality of Mana during
the different missions in FG, but it has not been possible to integrate such aspects under a
quantitative form. For instance, it is important to consider that, in relation to the vulnerability
of local population, in FG, and particularly in Mana RB, there are some areas of informal human
settlements (approximately along the roads D8-9 and 10). These areas are extremely dynamic,
and they rely considerably on the resources provided by the Mana river. Furthermore, more
than 2,000 illegal gold miners are spread across the municipality in inner forest areas. Also, the
municipal services emphasize as well the necessity to consider the potential cumulative
impacts of the upstream agricultural areas on the increasing water pollution in the area of
Javouhey (Fig. 19).

Another aspect concern drinking water points. The municipality is changing during these years
the method for drinking water provisioning switching from surface water to water sinks drilled
near the main village. At the moment the water provisioning is starting to rely on underground
wells in order to not depend on the surface waters of Mana river. Therefore, the vulnerability
assessment of drinking water availability must consider the technical features related to the engineered structures used to provide drinking water and their protection measures. Furthermore, Mana municipality currently provides drinking water to another different municipality, Awala-Yalimapo (Fig. 19), which would enlarge the indirectly impacted area if this water catchment point was affected by the flooded area.

Therefore, territorial vulnerability is dynamic, and its estimation should be updated through continuous visits on the field, surveys and interviews with local stakeholders. Furthermore, new parameters should be added to detail the vulnerability of specific assets. For instance, the vulnerability of infrastructures should include data concerning the construction materials of each building, their age, etc. Also, for the assessment of surface water quality vulnerability, the length of the impacted water streams, the water flow and the width of the riverbed, the distance from the dam etc. An interesting way to do so would be to integrate and quantify the level of vulnerability based on the Strahler order, which is a mathematical tree giving the numerical measure of the branching complexity of a hydrographic network.

### 3.3. Size vs quantity of the gold mine-types and cumulative consequences

In this study, the main output that greatly depends on the gold mine type, its features and localization, hence, on the TMS, is the flooded area. Therefore, as it is expected, the more the gold mine has higher volumes in terms of gold processing and tailings stored in the embankment, the greater is the flooded area if the mining dam fails: the rate of gold production influences the volume of tailings processing wastes, which is proportional to the potential flooded area if a dam failure would occur by overtopping.

Thus, on one hand the great *size* of the gold mine is defined in terms of gold production, the availability of important financial and human resources (e.g. capital, workforce, technical skills) and, in the current case, by its soil footprint. If currently a “greater” gold production influences the volume of tailings and the surface of the potentially flooded area, important questions should be raised about the allocation of project resources. A better allocation of human and financial capital of a mining company could aim to increase the operator performance in managing mining wastes through reducing waste inputs, optimizing storing methods, safety barriers reducing the probability of failure of a mining dam etc. Therefore, in this case, the *size* of a gold mine might not be directly proportional to the quantity of tailings stored and potentially hazardous. For instance, the analysis would be different if underground gold mines were considered in this study. Waste management in underground mining differs from the one during surface mining and, moreover, the surface of the mine site is likely to be much lower than the current gold mines in FG.

The other element that needs to be taken into account is the *quantity* of gold mines operating at scale of Mana river basin according to our TMS. This implies the consideration of cumulative consequences. For instance, in this demonstrative application, despite one LSM project-type has a greater flooded area in the case of a dam failure, the same scenario for
smaller projects (i.e. ASM or MSM-types) results in much smaller flooded areas but a considerably higher number of surface water affected. At the scale of the water catchment, and particularly in equatorial and tropical region such FG, a fundamental issue concerns whether the distribution of a given number of mine-types is higher or lower than the density of the hydrographic network. As it has been pointed out in the sub-section 2.2 of this Chapter, the influence of the number and types of mines chosen naturally influences the level of risk. The distribution of multiple gold mines on the territory, that we called *mining density*, increases obviously the probability to have at least on failure and more important cumulative positive and negative risks than the scenarios where only one mining project is considered. What is important to apprehend is if the cumulative risks of dozens of small mines can outweigh the risks generated by one large mine. For instance, Franks et al., (2013) highlight the implication of the consideration of cumulative risks into policies and management, through the necessity of decision-making that responds to the scale of the system in which impacts are aggregating and interacting, adopting participative and cooperating strategies for a management which is adapted to the studied system and its components.

The quantity of gold mines in each TMS is particularly relevant for the level of consequences in both the risk scenarios. In particular, the final score of the RSa integrates as well the cumulative consequences generated by a mining dam failure, which is an *extraordinary* event, that would occur for multiple mining projects at the same time. On one hand, history of accidental dam failures shows that is extremely rare that multiple dam failures occur at the same time. For instance, during 1960s, this happened in some South American areas, triggered by seismic events in the region (Fig. 46). On the other hand, this study shows the application of an approach to foresight and discuss potential scenarios at the territory level. Indeed, as mentioned in multiple occasions, the proposed approach does not aim to assess the detailed level of risk – for which specific methods exist already – but to compare territorial scenarios based on a certain level of risk and discuss them based on the level of detail of the assessment and its level of uncertainty. The main goal is to simulate and compare potential scenarios rather than precisely predict them. Hence, the current thesis aims to show an analytical approach – rather than a predictive one – in order to compare potential strategies for future land-planning scenarios involving (or not) mining activities of different natures.

### 3.4. Probability assessment and safety measures in the RSa

It has been shown in Chapter 7 that probability of occurrence has been integrated in this study based on a simple assumption that considers the probability of occurrence indirectly proportional to the *size* of the gold mine-type. The probability scores used in this study tend to significantly decrease the TMS scores. As mentioned, a global higher probability of occurrence of the dam failure event is assumed here (Table 24). Nevertheless, other assumptions could have been applied: for instance, a scenario (RSa_2), where the
probability is proportional to the size, productivity and financial resources of the gold mine or another scenario assuming a global lower probability of occurrence of the dam failure event (RSa_3) (Table 39). Therefore, it is important to emphasize the shortcomings present in the application shown in this study that might have led to the under/overestimation of the final RSa scores and how to address them.

<table><caption>Table 39 Assumptions concerning the probability scores in this study for the RSo and RSa_1 and other assumptions that could have been used</caption><thead><tr><th rowspan="2">Risk scenario</th><th rowspan="2">Assumptions</th><th colspan="5">Probability scores</th></tr><tr><th>i-ASM</th><th>ASM</th><th>MSMg</th><th>MSMc</th><th>LSM</th></tr></thead><tbody><tr><td>RSo</td><td>Certain occurrence of the risk events</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>RSa_1</td><td>Overall higher probability of occurrence of the event</td><td>1</td><td>0.75</td><td>0.5</td><td>0.5</td><td>0.5</td></tr><tr><td>RSa_2</td><td>Higher probability for less equipped projects</td><td>0.75</td><td>0.5</td><td>0.5</td><td>0.25</td><td>0.25</td></tr><tr><td>RSa_3</td><td>Overall low probability of occurrence of the event</td><td>0.5</td><td>0.25</td><td>0.25</td><td>0.25</td><td>0.25</td></tr></tbody></table>

First of all, the Bow-Tie diagram has been used in the current application only for its conceptual advantages, since it allows to display the cause-effect relationships between the triggering factors and the consequences around a central risk event. However, if used properly, the Bow-Tie might offer an important assessing tool that allows the assessment not only of the probability of occurrence of the main central event – in this case the dam failure by overtopping – but also the probability of occurrence of each consequence generated from this event.

This is particularly useful for a territory-based approach, it is not only important to understand what is the probability of the event to occur but also which is the probability for the consequences to overcome the mining site (or, even more specifically, the area surrounding the tailings dam, in case of the dam failure). For instance, the probability of occurrence of a dam failure in a given mine could be certain, but the probability for the failure to affect local population or the downstream waterbodies is inexistent (e.g. because the region is not populated or because of the construction emergency ponds). Therefore, the probability for the consequences to occur, after the central event occurs, may depend on the characteristics of the territory system and its level of socio-ecological vulnerability (e.g. region with no population) or on the characteristics of the mining system, i.e. the implementation of technical and organizational safety measures. Proactive and reactive safety measures can be effectively integrated within the Bow-Tie. This would enhance the results of the assessment and, thus, a pertinent comparison of the TMS risk scores and more effective decision-making support. For instance, the integration of these measures (e.g. draining or overflow devices, monitoring, alarming system) might suggest which actions should be proposed on the mining infrastructures or at the level of the river basin also to reduce their vulnerability. For instance, overtopping may occur and destroy the dam but if specific safety measures are implemented in the immediate downstream, the failure will stop at the mine site. In a certain way, the mining dam represents by itself a safety measure, at the scale of the mine site, to protect the
surrounding environment from the wastes generated by mineral processing. At the beginning of the century, tailings were released directly in water streams without any consideration for their socio-environmental consequences.

Finally, concerning the estimation of the probability of failure of tailings dams and its uncertainty, an international project called IMAWARE (https://riskinstitute.uk/imaware/) is currently being conducted by the Risk Institute of Liverpool University in collaboration with other universities, research centers and mining operators. The project focuses on a transdisciplinary approach to address mining risks in vulnerable regions, with a specific focus of South America. IMAWARE aims to enhance multi-stakeholder governance, dam reliability assessment, through stochastic structural reliability analysis of the physical dam under its environmental conditions, but also vulnerabilities, long-term human health and ecological risks in order to consider the entire nexus of external drivers, human factors, and engineering failure. This could represent a complementary tool to our approach, especially when the RSa chosen concerns tailings dam failure.

### 3.5. An alternative way to integrate probability within the RSa score

The method used in this study to evaluate the RSa score (Chapter 7) can be discussed for the fact that probability scores are integrated only at the end of the assessment. This means that this method does not consider that in the TMS has different probability of occurrence of flooded areas exist, according to each gold mine-type and their related probability scores (Table 24). As mentioned in chapter 7, section 7, another way to proceed is to integrate the probability at the beginning of the assessment (Fig. 70), assigning the probability scores to each flooded area per gold-mine type in each TMS. The probability scores are then multiplied to the maximal value of the geometric means of the vulnerability scores located within the flooded areas.
This second method allows a more rapid assessment and also a better spatialization of the final results through risk maps whose values change according to the gold-mine type at the origin of the flood event (Fig. 70d). Nevertheless, all these results are then reduced to non-spatialized RSa scores, as for the first method, in order to compare the TMS.

Furthermore, this alternative might present the same level of incongruity than the first method used in Chapter 7. Indeed, the method tends to lower the values, increasing the final (positive) RSa scores for each TMS (Table 26). It is significant that, at this stage, this second method does not consider the surface of the flooded area. For such reasons, the TMS-A, which presents the larger flooded area according to this study, has a better final RSa score (Table 26). Therefore, further improvements must prioritize these gaps and address such challenges through the development of approaches integrating in this second alternative the scores related to the surface of the flooded areas for each gold mine-type of each TMS. As mentioned already, specific aggregation methods should target the mixed scenarios, where multiple scores, related to different coexistent gold mine-types, need to be combined.
4. Final TMS scores and weighting coefficients

Once the risk scores of the risk scenarios were estimated, they were combined to obtain a final score for each TMS (Chapter 9). This score depends directly on the assessment of the risk scenarios and on how these two are aggregated but also on the weighting coefficients developed to account for the different perspectives of local stakeholders. The following sub-sections discuss the final scores of the RSa and the RSo developed in this study and the methods used for their aggregation and its weighting.

4.1. The non-weighted global scores of each TMS

4.1.1. Final risk scores of the RSa

As a demonstrative example, the most “preferable” TMS for the RSa scores assessed in this study are TMS-B and TMS-C (Table 33), while the illegal and the large-scale scenarios present lower scores. For the TMS-B and TMS-C, this is due especially because of the influence of the RSa scores, with limited surfaces of the flooded area. However, when weighted, the TMS-A score is significantly increased, particularly because of the influence of the limited surface (i.e. landscape degradation) in the RSo and the important weight associated (Table 20). This aspect is particularly interesting in FG, where at the moment, legal gold mining implies exclusively surface mining techniques that consume considerable cumulative surfaces. Could underground mining be a way to reduce such risks or they would just be replaced by new risks associated with underground mining?

In the case of the illegal scenario, such score is given by the fact that, despite the flooded area of each i-ASM individually is not comparable with the one generated by one LSM-type (size), the presence of 758 i-ASM mines (quantity) generates cumulatively a wide flooded area. Furthermore, because of the lack of safety measures in i-ASM, the probability of occurrence of the failure is more likely. Nevertheless, the TMS-A and TMS-illegal have the same final RSa score because in this study, the final RSa score is the result of the consequences (i.e. the combination of the level of vulnerable assets within the impacted area and the size of the surface area) and the probability. However, probability scores have been applied only to the level of vulnerability of the assets within the flooded area but not to the "surface of the flooded area" score. Since the probability score is very low for the LSM (Table 24), this would have actually optimized significantly its final score of the TMS-A.

The integration of the probability scores to the “impacted surface” score represents still an unresolved issue. Except implying supplementary aggregating decisions that, at this stage, would make the results more uncertain, a specific difficulty concerns particularly the mixed scenario (TMS-D) where different mine-types present different impacted surfaces and different probability of occurrence of the failure. For this reason, more pertinent ways to adapt the probability parameter to the coexistence of different mine-types within the same TMS must be developed. Qualitative and expert-based assumptions could be used to assign to each mine-
type more valid scores representing the probability of failure and more composite ways to aggregate them. Or else, it could be interesting to use the alternative approach (section 2.3) to integrate the probability scores directly in the impacted area and finally, normalize the final RSa scores by the flooded surfaces corresponding to each gold-mine type.

A specific concern is related, for the application shown in this study, to the intensity of the consequences of the dam failure on surface water resources. This aspect is to be taken into account especially in regions such as FG where the hydrographic network is extremely dense and particularly for the scenarios involving gold mines of smaller *sizes* (e.g. ASM, i-ASM) but in a greater *quantity*. As mentioned, independently from their size, a higher number of mines heterogeneously located on the river basin, is more likely to be in contact with a greater number of water streams. In FG, the main negative consequence generated by ASM is the increase of water turbidity and silting through the production of sediments, due to accelerated soil erosion. While the surface of the individual flooded areas would be very low, their cumulative areas and consequences would be very relevant. Therefore, the integration of such particularisms would change significantly the final global RSa scores of each TMS. This aspect justifies again the need of studies which focus, rather than at the mine-itself, at the territory level and the cumulative dimension of risks. Specific indicators could be developed to assess such consequences, for instance focusing on stream length, water flow, Strahler order of each segment but also on the quantity of released materials in water streams and their chemical nature.

### 4.1.2. Final risk score of the RSo

From a general point of view, the TMS-A displays the “best” RSo score, thus showing that for the same annual gold production of 5,000 kg of gold, cumulative risks of smaller projects might outweigh the risks generated by only one LSM-type gold mine. The higher RSo score for the TMS-A is due particularly to the lower land consumption compared to the cumulative surfaces occupied by multiple MSM, ASM or i-ASM projects. Thus, it seems that, in normal functioning conditions, the production of gold generated by only one cluster of mines (i.e. the LSM in the TMS-A) could represent a better allocation of land-use, avoiding supplementary soil losses. The MSMc scenario (TMS-B) shows as well a high score, mainly because of its employment potential which is the highest among the considered legal scenarios, based on the parameters that we have defined in Chapter 4.

However, such statements should be considered carefully because of the uncertainty related to the development of the RSo that, as mentioned, was not the main focus of this study. Compared to the RSa, the RSo used is poorly developed and it has been integrated to compare the TMS based on their global scores, in order to show the feasibility of the approach proposed in this study. Therefore, further studies must focus on the improvement specifically of the RSo development and the assessment of its level.
A first point of discussion of the RSo concerns its definition as a “risk” scenario. It has been mentioned already that the risks related to the normal functioning of a mining project are not properly “risks”, strictly speaking, since their occurrence is certain. Therefore, these “risks” represent rather “nuisances”, when negative (e.g. deforestation, noise pollution), or opportunities when positive (e.g. employment, economic outcomes) that arise as the mining project exists. However, what is uncertain is the occurrence of the positive or negative consequences that might be generated by these events. For instance, the deforestation generated by the development a mine site could be considered not as a risk, since it is certain. On the contrary, the potential effects that such deforestation has on biodiversity, CO2 sequestration or else, can be considered as such. Therefore, the RSo should focus also on the assessment of the uncertain events that are generated by the certain nuisances or opportunities related to the mere existence of a mining project. This cause-effect analysis should be performed through the integration, within the RSo, of the Bow-Tie analysis for each risk event considered. Also, in this study, the estimation of the impacted area and territorial vulnerability have been performed only for the RSa and no mention has been done for the assessment of the final RSo score. These two aspects have been considered essential for the application of the approach proposed in this thesis. Therefore, further studies should focus on the quantification of the interactions between the events occurring during the normal functioning of mining projects and the uncertain events they might generate, the area affected by these events and, therefore, the vulnerability of the assets within it. An option could be the adaptation of LCA studies to our approach for the estimation of the risk score in normal operating conditions.

Furthermore, the distinction between what is a positive and what is a negative risk must be multi-perspective. This means that stakeholders should be integrated in order to understand if there is a unanimous consideration of the negative and positive risks related to a mining project.

Other considerations are related to the choices made to expose the development of the RSo and the assessment of its score must be discussed in order to understand the final score obtained. The RSo was developed in this study including only one negative risk (i.e. deforestation). The surface deforested due to the development of the mine site has been considered as a global indicator to assess the overall negative environmental footprint of a gold mine. Indeed, it might represent the immediate physical effect of the development of a mining project in a given area. However, the selection of only one negative risk, compared to four positive risk events (see Chapter 8) is not considered sufficient to obtain a reliable score of the RSo. The disproportionate combination of such risks or the weights that are given to them might mislead the resulting scores. For instance, even if the negative consequence had the lowest value, this would be compensated by the presence of four positive impacts.
Unlike the present study, that focused on the available data for a rapid estimation of the RSo scores, the assessment should consider more trustful indicators. These indicators can be found in general literature not specific to the mining sector, often at the country-scale. For instance, concerning employment, the International Labour Organization proposes indicators such as the number of employed or non-employed persons, to measure employment opportunities and quality (Coderre-Proulx et al., 2018). De Bustillo Llorente and Fernandez Macias, (2005) assess employment opportunities in Spain through the quantification of multiple indicators (e.g. salary, teamwork, type of contract, public or private sector, length of service, size of the workplace). Other indicators are proposed by The World Bank Group and the Organisation for Economic Co-operation and Development (Lachler and Merotto, 2020; OECD, 2020).

The initial data used for the estimation of the indicators and the thresholds classes used for their scoring are a limiting factor that influences the final results. Furthermore, the uncertainty related to such data must be accounted for. For instance, for some risks of the RSo, data are available for some gold mine-types but unavailable for others. In this case, the result would be the comparison of TMS where in some cases, the assessment is partial or even impossible to perform. A simple way to address these shortcomings could be the quantification of an index considering the number of quantifiable risks for each TMS based on the rate between the number risks with available data and the total number of considered risks. For instance, one TMS is provided with all the available information for the assessment of its RSo score (let's assume that the RSo is composed of 10 consequences), while another TMS disposes only of 8 quantifiable consequences, the index would be respectively 1 for the first one and 0.8 (i.e. 8/10) for the second one. This index would then be multiplied to the global RSo for each corresponding TMS in order to obtain a result which at least would be weighted according to the level of available information for each TMS. In any case, further studies should focus on gathering more reliable data and to develop assessment methods to define more accurately specific indicators in FG and their thresholds ranges based on participative approaches and expert-based considerations.

Finally, the methods used to aggregate these data need as well improvements to consider the number of considered risks, whether they are negative or positive, and the weights potentially given by stakeholders. For instance, if the maximum value was chosen to combine total negative and positive scores, the best scenario would be TMS-B, since it is the most performant on the positive consequences due to its highest score corresponding to the creation of job opportunities. Indeed, in this scenario, 6 MSMc-types might generate a significant rate of employment due both to the recruiting of miners and specific technicians for the functioning of six different cyanidation plants.
### 4.1.3. Aggregation of the RSa and RSo scores

As shown in Fig. 73, the main aggregation method used to combine RSo and RSa scores is the geometric mean and the maximal value. The maximal value was only used in order to take into account the maximal score among all the geometric means of each spatialized layers assessed during the RSa (see Chapter 7).

The geometric mean has been chosen since, although it is less sensitive to different scales and only applicable to positive numbers, the geometric mean is preferred when synthesizing judgment for decision-making purposes (Krejci and Stoklasa, 2018; Doré-Ossipyan and Sareh, 2020). Indeed, the geometric mean is directly based on all the observations and usually the presence of a few extremely small or large values has no considerable effect on the result. Concretely, the geometric mean allows to reduce the influence of the highest values of the dataset and to increase the one of the lowest ones. If the modeler needed to increase such contrast between highest and lowest values, another aggregation method could have been the harmonic mean. It is important for further applications, to integrate decision-making tools (e.g. AHP, TOPSIS) allowing data combination through participative and multi-criteria approaches.

### 4.2. Weighting coefficients and the integration of stakeholders' perception

The multi-perspective dimension of the approach proposed in this study led to the integration of weighting coefficients to the global scores of each TMS. These coefficients have been obtained through a questionary survey among local stakeholders, as it is detailed in Chapter 9.

However, the number of gathered answers does not represent a sufficient sample size and have high interval coefficients. If we consider the size of the population in Mana river basin (~12,000 inhabitants), at least 1,000 answers would represent a reliable sample size that guarantees a sample error inferior to 3% at a 95% confidence interval and with a sample proportion of 50% (Israel, 1992). At this stage, the number of answers is not sufficient to integrate a correlation analysis as well to quantify the correlation between the weighting coefficients and the TMS scores (e.g. Spearman or Pearson coefficients).

Furthermore, the weighting coefficients, as shown in Table 34, tend to have the lowest values, particularly for mining-related assets, while the ecological assets were the most "important" for respondents. At this stage, this can be explained by several considerations.

- First of all – and most importantly – only one respondent belongs to the mining operator's actor-category, which might drive and counterbalance a significant variation of the coefficients.

- Secondly, socio-ecological assets represent public goods and natural assets of general interest. In this sense, the responses to the questionary seem not to
consider mining projects as matter of land planning and thus, at least with the same
relevance that is given, for instance, to public roads and infrastructures.

- Also, as we mentioned, the gold commodity represents a particular strategic asset that does not awaken often the interest of local populations for its actual use and applications as raw material, unlike other commodities with higher technological applications (e.g. niobium, coltan, copper, zinc, nickel).

- Finally, most of the responses came from inhabitants of Mana municipality, which is currently affected by the presence of approximatively 2,000 illegal gold miners, which might affect the vision of local communities towards mining activities.

Therefore, further actions should look towards the optimization of the questionnaire survey in order to gather as many answers as possible from all the different stakeholders in order to obtain reliable coefficients for a more pertinent weighting process. Despite the need for further improvements of the survey, the purpose of this weighting step is to show the methodological feasibility of the integration of stakeholder's perception in order to normalize the results of the assessment and of the global score of each TMS based on stakeholder's perception.

5. Comparison of the territorial scenarios: which scenario is the “best”?

The “best” scenario of this current application, according to the choices made, are the TMS-B and the TMS-A, suggesting that, for an annual gold production of 5,000 kg, the most interesting strategy based on the two risk scenarios developed, is represented alternatively by the development of 6 MSMc mines or one LSM in Mana river basin. Therefore, are these the options that a potential stakeholder should choose to develop mining strategies in the studied area?

The answer is negative because, as mentioned, the application shown in this study allowed to show the rapidity and feasibility of the approach that we propose and its adaptability to existing data. The result would be much more reliable if an uncertainty analysis would be performed, for instance through Monte Carlo simulations, fuzzy logic-based methods, uncertainty propagation, Bayesian statistics, although they might introduce subjectivity (Awuah-Offei and Adekpedjou, 2011).

Furthermore, as detailed in section 2.1., the comparison of the territorial objectives and the corresponding values for each TMS could be a preliminary tool that might allow the comparison and ranking of the different strategies (Table 37). The “best” strategy must be evaluated on multiple criteria which include territorial objectives as well as the generated and suffered risks by the mining projects (e.g. opportunities and losses) and their level of uncertainty.

Furthermore, except the illegal TMS, all the legal TMS (i.e. A, B, C, D) present very similar final weighted global scores which range between 2.7 and 2.9, as it has been explained in the
previous sections. The differences between these TMS is not significant if we consider the uncertainty behind the assessment of their scores. However, the illegal scenario obtains the worst score among all the selected TMS. Indeed, the main point of force of the illegal TMS is represented by its aptitude to generate informal employment, influencing considerably the RSo score (Table 40). However, the surface impacts of the illegal TMS are the highest among all the other scenarios. Nevertheless, the social phenomenon of gold illegal mining must be considered extremely carefully since it is not subject to the same considerations that can describe legal gold mines, particularly concerning the precise delimitation of “mining sites” and “mining projects and the social – rather than technical – connotation that this phenomenon take locally in FG and in many other countries.

<table><caption>Table 40 Ranking of the TMS developed in this study based on their final global score with and without the integration of stakeholders' perception</caption><thead><tr><td>Ranking</td><td>TMS</td><td>Weighted TMS global score</td><td>TMS global score</td></tr></thead><tbody><tr><td>1</td><td>TMS-B</td><td>2.9</td><td>3.4</td></tr><tr><td>1</td><td>TMS-A</td><td>2.9</td><td>3.3</td></tr><tr><td>2</td><td>TMS-C</td><td>2.7</td><td>3.2</td></tr><tr><td>2</td><td>TMS-D</td><td>2.7</td><td>3.2</td></tr><tr><td>3</td><td>TMS-illegal</td><td>1.9</td><td>2.3</td></tr></tbody></table>

## Conclusion and perspectives for the application of our approach in FG gold mining sector

The approach proposed in this thesis has been tested on the gold mining sector in FG. Rather than predicting risks related to gold mines in FG, the purpose of our approach was the comparison of five territorial scenarios, involving different types of gold mining distributed over a river basin. Despite the level of uncertainty, this study answers to the purpose of presenting an original approach to look at mining activities and their risks at the territory level for land-planning strategies. Its application shows its feasibility, especially in a territory such as FG where data are often not available.

This approach is not operationalizable yet but the current application was intended to show as well the open questions and challenges that might be addressed by further studies to enhance our approach. Further improvements should focus on the characterization of the uncertainty of the results. The results of the application shown in this thesis should be integrated with the potential error and their level of uncertainty. Indeed, uncertainty and sensitivity analysis would help quantifying the confidence of the results in order for the application to be more pertinent to support decision-making and land planning.
Among other perspectives, the current application should aim to:

1. Improve the process of data gathering for the assessment of the risks and for the definition of the features of gold mines in FG and socio-ecological vulnerability;

2. Develop new TMS, for instance combining the legal and illegal sectors in the same scenario, which would be a more representative scenario in FG. This means to mobilize pluridisciplinary competences, including social and human sciences, to apprehend the technical and social features of these activities, especially for the illegal gold mining sector;

3. Improve the ordinary risk scenario with more risk events, integrating a Bow-Tie analysis, the identification of the impacted areas and socio-ecological vulnerability;

4. Test new central events for the RSa (e.g. explosions, transport accidents, spills, pit instabilities) and/or potentially combine multiple RSa at the same time.

Finally, the application of our approach in FG should integrate as well new project-types that might respond to the same territorial objectives defined for the TMS. For instance, a great number of Classified Installations for Environmental Protection (ICPE) are located in FG, among which just a few are related to the mining sector (Appendix 16). It would be interesting to test the approach integrating some of these projects, their specificities and the relative regulatory frameworks. The consideration of projects of different natures and their risks would allow to meet in a more comprehensive way the purposes of land-planning of our approach.

For instance, recent experimental studies concern the potential implementation of agromining in FG. Agromining is a practice that allows the recovery of target metals through the utilization of hyperaccumulator plants whose biomass is harvested and processed (van der Ent et al., 2015). It would be a technique serving multiple purposes of production, through the extraction of strategic metals – although very limited compared to mining projects – through biogeochemical processes. Therefore, agromining might present an economic interest, since it would contribute to respond to the demand of metals such as gold, at a different ration. Furthermore, this practice might respond as well to technical and operational needs – for land reclamation, revegetalization and phytoremediation of mine sites, reducing their negative socio-environmental consequences (Chaney et al., 1997; Krisnayanti et al., 2016) – but also to the enhancement of ecosystem services (Pons et al., 2018).

Finally, despite the approach is specifically built for mining activities, the same framework could be applied in order to integrated mixed scenarios involving other anthropic activities that may have important socio-ecological consequences (e.g. biomass production, agricultural activities and farming) in order to: i) assess the overall level of cumulative impacts within a same territory; ii) analyze and discuss the closed interactions between mining projects and other human activities.
# Chapter 11. Advantages, limits, perspectives of our approach and concluding remarks

This study represents the first effort to propose a new and original approach to integrate mining activities into wider spatial scales of decision. In this PhD thesis, this takes the form of a comparison between multiple land-planning strategies for mining regions based on risk assessment at the territorial scale. Furthermore, the proposed approach was given a formal basis and has been operationalized through the development of a methodology based on existing methods and adapted for its application to the FG gold mining sector.

We are aware that there are still important efforts to be made at this stage. These efforts should focus on the improvement of the approach and its operationalization, addressing as well the challenges that have been pointed out in the previous chapter.

## 1. Added value of the approach proposed in this study

### 1.1. Mining as part of a land-planning strategy

The application to FG is based mainly on the general procedure followed to perform risk assessment, as defined by ISO (2008), namely: system definition, risk identification, analysis, quantification, management and control. Indeed, risk is one key-elements considered in project management. As for project management, when it comes to territorial management and land-planning, risk must be accounted in the interest of public authorities, for governance, and of mining operators. However, because of the very wide spatial scales addressed by our approach and because its purpose is not the quantification of the risk it-self but rather the comparison of different scenarios based on their risk scores, the standard risk assessment process has been adapted to our needs.

Our approach meets most of the objectives presented in Chapter 2. The land-planning purpose of our approach is emphasized in one of the crucial steps shown in Chapter 5, i.e. the development of different TMS defined in agreement with pre-determined territorial objectives.

Indeed:

i) It is structured and transversal, based on existing frameworks (such as ISO, 2008) and adapted to wider spatial scales;

ii) It allows the integration of the coexisting mines with different characteristics.;

iii) It integrates the socio-ecological vulnerability of the territory where mining is performed and risks of different natures

iv) Its application is based on currently available data;

v) It is susceptible to include the involvement of stakeholders all along its application;

vi) It provides results which can be communicated and easily made accessible to stakeholders (e.g. maps, sheets).
## 1.2. The ecosystem-services approach

According to our approach, a mining project is considered as a socio-technical system operating within a territorial socio-ecological system. This requires a systemic approach where a risk – positive or negative – occurs when the performances and functions of one of these systems are – positively or negatively – altered (Fig. 7 and Fig. 13). Therefore, the “good” functioning of a mining project depends on its positive or negative contribution to the socio-ecological system where mining is performed. The operationalization of this approach can be done through the integration of the ecosystem-services framework (see the Glossary). Despite subject to multiple debates regarding its fuzzy conceptuality and its application (Schroter et al., 2014) an ecosystem-services approach might:

i) Merge into a single concept both the social and ecological dimensions of the risk. The performances and functions of a territory system aim to satisfy specific needs (e.g. water provisioning, food quality, economic welfare). When mining alters the processes that guarantee such performances and functions, the corresponding needs might be unsatisfied (e.g. the release of important amounts of heavy metals-enriched sediments reduces surface water quality, jeopardizing the supply of drinking water of nearby settlements).

ii) Serve as an operational tool for decision-making support to help quantifying the transversal impacts of some human activities through scenario-based approaches at wider spatial scales (Furst et al., 2017; Scammacca, 2017; Blanchart, 2018).

The ecosystem-services approach should be furtherly improved through specific assessment methodologies that allow to apprehend the nexus among all the different ecosystem services and the mining activities. Therefore, further studies should focus on the quantification and spatialization of ecosystem-services *flows* (the ecosystem services actually delivered) and *stocks* (the ecosystem services potentially delivered), related to different ecological components (e.g. water, air, soils, forest, biodiversity). This would allow a state of the art of the original conditions of ecosystem services supply. In this way, the influence of different territorial mining scenarios on ecosystem services supply could be quantified more consistently.

For instance, a research project called ECOSEO is being led by the International service of the French National Forestry Office (ONFI) and the WWF. The purpose is to assess ecosystem services delivery at the scale of the Guiana Shield through remote-sensing and GIS tools (ONFI and WWF, 2019). This might provide a significant amount of data that could improve the estimation of socio-ecological vulnerability and, therefore, of the potential consequences generated by mining activities.
## 1.3. Complementarity between our approach and SWOT analysis

SWOT (Strengths-Weaknesses-Opportunities-Threats) analyses, as mentioned in Chapter 1, are another tool for the comparison of scenarios to support decision-making and strategies for project management but also land-planning. This tool is widely used in the mining sector as well (Azimi et al., 2011; Cluett, 2012; Neagu et al., 2015) and allows project managers to develop business goals while understanding the viability of a project, addressing weaknesses and capitalizing on project opportunities and strengths. However, SWOT analyses do not prioritize issues or provide solutions and alternative decisions, jeopardizing the effectiveness of decision-making with a great amount of useless information. Hence, in some cases, they can be integrated with other decision-making methods. For instance, Azimi et al., (2011) combined different multi-criteria decision-making methods such as the Analytical Network Process (ANP) and TOPSIS within the SWOT framework to prioritize the strategies of Iranian mining sector. The authors used SWOT analysis to assign feasible strategies and then, ANP and TOPSIS to rank different mining strategies.

These applications were performed at the scale of the mining projects, without acknowledging the territorial dimension of mining. Nevertheless, the SWOT analysis can be performed as well to suggest multiple development strategies at the territory level. For instance, Deloitte (2018) propose a SWOT analysis which compares the relevant economic sectors in FG in order to propose potential development strategies that might lean on wood production, tourism, construction, manufacturing or other sectors.

The SWOT analysis could be integrated as well within the proposed approach as a preliminary tool for the comparison and analysis of the different mine-types. The figure given in Appendix 17 gives an example of a SWOT analysis for the gold mine-types in FG, based on the perspective of mining operators. However, this analysis could be too much shallow at some occasions if not deepened with more quantitative information. Moreover, it might present further limitations regarding the assessment of the positive and negative outcomes of the classified mine types. Indeed, despite it integrates a qualitative and descriptive multi-perspective dimension, SWOT may not be sufficient to quantify and synthetize the influence of the different stakeholders' views on the final strategies, like what it has been proposed in this study through the weighting of the TMS scores (Chapter 9). Furthermore, the analysis of cumulative effects of multiple mine types on wider spatial scales is often limited in SWOT since it is often performed on the project alone. Also, spatial variability might be considered but SWOT is not based on geospatial data and, thus, it is not spatially explicit. Hence, the variability of the territorial specificities (i.e. socio-ecological vulnerability) are difficult to be integrated.

Hence, SWOT might not be appropriate to substitute alone the territorial approach proposed. Nevertheless, when integrated with sophisticated decision-making methods (e.g. ANP, AHP, TOPSIS), SWOT analysis can be important to develop and compare mining strategies.
For instance, within our approach, SWOT can be used as a preliminary complementary tool that can consistently support a first classification of the mine-types or the development of the TMS. If coupled to the analysis of the pre-determined territorial objectives and their corresponding scores (Table 33 and Table 37), SWOT can allow a preliminary comparison and discussion of the TMS, even before a detailed risk assessment.

## 1.4. The participation of stakeholders

Chapter 2 describes the ideal application of the current approach with a continuous participative integration of stakeholders all along the methodology. In this study, three categories of stakeholders have been identified: mining operators, public authorities and civil society (Table 5). Their integration in the current application has been pointed out at different steps, such as the selection of the territorial system, the validation of the gold mine-types and their features, risk identification, and the development of weighting coefficients.

Despite their partial integration, further efforts should be directed to involve stakeholders all along the application. Furthermore, the involvement of stakeholders and their contribution to the assessment should be more structured and quantified through transdisciplinary methods. For instance, the definition of territorial objectives should be strictly based on stakeholder's participation, since this is a crucial step for the development of the TMS. The same goes for the construction of the risk scenarios and the selection of the assessment indicators. Finally, once scored, the TMS should be publicly discussed and compared to integrate all the stakeholders at the same time into interactive exchanges, discussions and brainstorming. Further contribution of stakeholders to the application should be structured and quantified. Also, stakeholders might provide a considerable amount of data, in different forms and at different degrees of expertise.

However, it is important to point out that stakeholder's involvement must be considered carefully. From a general point of view, stakeholder's participation is a very delicate process which depends on a confidential relationship, transparency, available time, the scientific validity of their contributions and their representativeness, and on potential clashes and political implications among stakeholders (Voinov et al., 2016; Gotts et al., 2018).

## 1.5 Extendibility of the approach: other commodities, mining projects and territories

The objective of this study is not to develop a specific method for industrial risk assessment of mining projects but rather to propose an original approach to consider mining activities and their risks at the territory level. Hence, although our approach is partially operationalized through a methodology developed via its application test to gold mining in FG, the purpose of this approach is to be transposable to other commodities, types of mining activities and other territories. For instance, the development of TMS based on a standardized
mine-types might be performed in other cases and for commodities other than gold. The classification is based on the factors that the most influence risk occurrence or intensity (e.g. exploitation and processing techniques, type of geological deposit) Although the results of the classification will obviously differ from the ones presented in this study, the approach and the methodological framework should be the same, we believe.

However, the variability of the mining system and the extracted commodity is not the only parameter that needs to be taken into account. It has been shown that the specificities of the territorial system in which mining is performed – its juridical and political frameworks, its socio-ecological vulnerability – have a significant influence on the mining system itself and its negative or positive risks. For instance, the methodology proposed within this study can be extended to other territories where gold mining is performed. As mentioned, Suriname gold mining, although within the same geological settings of FG, involves different types of gold mines that might add to the ones currently operating in FG (i.e. gold dredging). Also new criteria of distinction should be applied to classify these gold mine types since gold-Hg amalgam is not used only by i-ASM in Suriname. In some regions with different climatic conditions and with almost absent population density where gold is exploited, such as the Northern Canada, the socio-ecological vulnerability of the territory will not be comparable to the characteristics of Mana river basin or other mining territories in the Amazon region.

## 2. Geospatial issues of the approach and its application

Because of its territorial dimension, our approach is supposed to be spatially explicit, in order to account for the spatial variability of risks and to serve land-planning support purposes. Furthermore, maps are a tool for a visual communicability, displaying and sharing information that otherwise might be inaccessible to the general public. In this study, QGis has been used to process geospatial data for the RSa, but improvements should address the application of spatialized data (e.g. socio-ecological vulnerability, impacted area) for the RSo.

### 2.1. Mining siting for the development of the TMS

As mentioned, the localization of the mines during the development of the TMS might have a significant influence on the final results and TMS scores. Since it is not the purpose of this thesis, a simple methodology is proposed in Chapter 5 to identify the suitable areas for the localization of gold mine-types. However, further studies should consider:

i) The improvement of more criteria to identify the suitable areas and reduce arbitrary or random decisions for the precise siting of the mines within these areas;

ii) Quantify the uncertainty of criteria used for mining localization and the influence of mine localization on the final TMS score, for instance using correlation or sensitivity analysis;
iii) When possible, the integration of existing and validated protocols for mining siting. For instance, it has been already mentioned that at the moment in FG, the French Geological Survey is developing such a methodology within the research project APPUI-Mines, which might be a useful complementary tool to the approach proposed in this study, integrating more “favorability” criteria for mining siting.

## 2.2. How to integrate risks at the project-scale in a territory-based approach?

Our approach analyzes one or multiple mines in each TMS, and performs a sector-based, rather than project-based, assessment. This implies necessarily the simplification of the mining system and its features, reducing the technical discrepancies between two distinguished projects that might be associate to a same mine-type. This is the very logic behind the classification of mine types proposed in this study.

Nevertheless, it is important that this simplification – realized through the standardization of the mine type – should not influence considerably the scores of the risk scenarios. Indeed, in reality, each project depends on specific topography and logistical specificities that affect the organization of the mine site and, thus, are essential to apprehend for a more precise assessment of the related risks.

Therefore, the integration, within the current approach, of the risks suffered at the project-scale might be an important challenge to address in the future, since it would need very accurate information. The same accurate information that has been lost during the standardization of mine types. Thus, it is difficult to combine the territorial purposes of our approach with too much detailed project-scale data.

The assessment at the project-scale, as individual unit, is already performed through several methods mentioned in Chapter 1. Somehow, such methods should be integrated in our approach to acknowledge the business-risk of mining operators generated by the failure of their technical and socio-environmental performances. For instance, the estimation of *cumulative risks* related to multiple mines would be then biased if the mines were not standardized and normalized. As example, the consequences for the territory due to the dam failure of an LSM or an ASM type can be significantly different. Nevertheless, the consequences of this failure event for the mining operator due to the destruction of the dam structure (after a dam failure) would be equally important in the case of an ASM (small dam) as for an LSM (big dam) operator. Their financial vulnerability (e.g. economic and human resources) would be equally impacted since an ASM would not have the same economic means of an LSM mine.

Furthermore, the territorial objectives on which based the TMS are not based at the scale of the projects but of the territory it-self. Mining operators’ perspectives – related to the performance of a mining project also as business activity – can be integrated within these objectives?. Such integration must pass through a systemic approach quantifying the
interactions and the feedback loops between the mine and the territory, i.e. the positive and
negative contributions of the mining project to the performances of a territory (e.g.
environmental protection, employment, economic development) and the contribution of a
territory to the mining project (e.g. regulatory frameworks, political stability, mining
contestation).

2.3. *The problem of transboundary risks*

The spatial variability of the risks related to mining projects can be discussed also with regard to the acknowledgment of their transboundary nature. Indeed, risks do not submit to administrative boundaries and their consequences – or else the triggering factors – might vary in quantity and quality go beyond the boundaries of a given territory system. For this reason, the definition of the territory in which the TMS needs to be developed is a key-issue for the methodological feasibility of our approach. However, this represents at the same time an operational gap that need to be considered for decision-making support. Indeed, the comparison of the TMS for land-planning purposes cannot overlook the transboundary nature of some risks, although these are outside the pre-defined system boundaries. This would be regrettable for governance perspectives.

For instance, the current application focuses on Mana river basin as territorial unit
system. However, especially when outside the flooded area, several consequences might
impact considerably and exceed the perimeter of the river basin as well (e.g. communicating
roads, tourism, drinking water points etc.). Moreover, in FG, territory-based risk assessments
of gold mining activities and the development of potential land-planning scenarios must
consider the existence of transboundary flows of resources among different countries in the
Guiana Shield. As mentioned, the focus on the Maroni river basin in FG should integrate the
Surinamese gold mining sector as well, since the larger part of Maroni river basin is located in
Suriname. Therefore, system definition is fundamental for the application of the methodology,
but it should not be limiting factor for the interpretation of its results, especially when it comes
to decision-making and governance. Each application of the proposed approach must be open
to discussion and interpretations that cannot neglect such transboundary aspects, especially
when it comes to develop territorial strategies.

3. Future perspectives of our approach

3.1. *Uncertainty and sensitivity analysis of the methodology*

Our approach and the method that has been presented in this study to apply it to FG is
adapted to the amount of available data, as it has been shown in the application given in this
thesis. Therefore, it is important to quantify the reliability of the results of the proposed
methodology according to the level of available data. For instance, the results presented
suggest that TMS-B is the best scenario according to the risk events selected in this study. However, as discussed in the previous chapter, the scores of each TMS cannot must be considered carefully at this stage. Indeed, the pertinence of these scores for decision-making purposes must account of the potential error and their level of uncertainty of the results. Indeed, the application of uncertainty and sensitivity analysis should help quantifying the confidence of the results in order for the approach to be more appropriately used to support public authorities, land-planners but also mining operators and civil society.

Therefore, an important challenge to address to compare and rank the assessed TMS based on their scores is to improve the reliability of the such scores through the estimation of their level of uncertainty. Sensitivity and uncertainty analysis can be performed using stochastic modeling such as Monte Carlo simulation or, when limited data are available, fuzzy logic-based methods, uncertainty propagation, Bayesian statistics, although they might introduce subjectivity (Awuah-Offei and Adekpedjou, 2011).

## 3.2. The role of safety measures

Mining risks, positive or negative they might be, depend on the intrinsic characteristic of the mining project(s). Among these characteristics, an important attention must be given to the technical and organizational measures implemented by mining operators to improve mining performances limiting the risk event from occurring (i.e. proactive measures) and/o reducing its consequences once it occurs (i.e. reactive measures).

The role of these safety measures has been already pointed out in this thesis at multiple occasions. For the risk assessment to be reliable and for a correct application of the Bow-Tie, the capability of each mining project to implement safety measures must be quantitatively accounted for. Indeed, these measures might completely alter the final scores of each TM.

Table 41 shows a non-exhaustive list of some of proactive and reactive safety measures that are implemented by gold mines in FG. Future studies should focus on the identifying and detail such measures for each mine-type and their degree of effectiveness. Moreover, specific methods should be suggested in order to quantify the role of these measures. In industrial risk management, the Bow-Tie analysis might be a useful tool but it needs important amount of data for a pertinent estimation.
<table><caption>Table 41 List of general safety measures used for gold mining projects in FG. The information concerning the susceptibility of each gold mine-type to implement such measures is not available at the stage of this current thesis</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Proactive Safety Measures (dam failure scenario)</strong></td><td></td></tr><tr><td><strong>Technical</strong></td><td></td></tr><tr><td>Overflow device</td><td></td></tr><tr><td>Vegetation cover</td><td></td></tr><tr><td>Spillway system</td><td></td></tr><tr><td>Bottom draining device</td><td></td></tr><tr><td>Dry disposal of the tailings</td><td></td></tr><tr><td>Tailings thickening</td><td></td></tr><tr><td>Size-differentiated disposal of the tailings</td><td></td></tr><tr><td>Cementation (increasing impoundment resistance)</td><td></td></tr><tr><td>Coarse granular fill covering slopes against erosion</td><td></td></tr><tr><td>Geotextiles and geosynthetics</td><td></td></tr><tr><td>Freeboard control</td><td></td></tr><tr><td>Downstream dam construction</td><td></td></tr><tr><td>Risk and Impact assessment studies</td><td></td></tr><tr><td><strong>Organizational and Human</strong></td><td></td></tr><tr><td>Monitoring and visual inspections</td><td></td></tr><tr><td>Technically formed and skilled employees</td><td></td></tr><tr><td>Crisis simulation training</td><td></td></tr><tr><td>Processing centers for i-ASM and ASM (Hinton et al., 2003)</td><td></td></tr><tr><td><strong>Reactive Safety Measures (dam failure scenario)</strong></td><td></td></tr><tr><td><strong>Technical</strong></td><td></td></tr><tr><td>Emergency water pond</td><td></td></tr><tr><td>Alarm system</td><td></td></tr><tr><td>Containment barriers</td><td></td></tr><tr><td>Addition of chemical to reduce/neutralize contaminants</td><td></td></tr><tr><td>Application of OM for chemical stabilization</td><td></td></tr><tr><td>Removal of spilled tailings</td><td></td></tr><tr><td>Spillway system</td><td></td></tr><tr><td><strong>Organizational and Human</strong></td><td></td></tr><tr><td>Evacuation measures</td><td></td></tr><tr><td>Insurance</td><td></td></tr><tr><td><strong>Restoring Safety Measures (dam failure scenario)</strong></td><td></td></tr><tr><td>Revegetalization*</td><td></td></tr><tr><td>Compensation measures</td><td></td></tr></tbody></table>

### 3.3. Suffered risks and social acceptability of mining
For the construction of the risk scenarios, our approach wants to include all the generated positive and negative outcomes by mining but also the events suffered by mining operators themselves. However, as partially discussed in section 2.2. of this chapter, in this study the consequences suffered by the mining project(s) have not been estimated because of the mentioned geospatial issues.

Indeed, it is difficult to apprehend and quantify the risks suffered by a mining project, since they significantly depend on project-centered specificities and cannot be integrated in a territory-based approach. This means that the estimation of the cumulative risk suffered by multiple mine types in a same TMS might be very complex since it depends on the specific features of each mine type. Furthermore, the estimation of these risks often implies the possession of a series of financial and investment corporative data, hard to find or even confidential.

Nevertheless, it has been said that the performance of a mining project relies on its contribution to the performances of the territory in which its located. This means that, some risks suffered by the mining projects are the result of feedbacks from the positive and negative risks that mine themselves generate on the territory. For example, environmental degradation generated by mining can lead to contestations and conflictual situations that might represent an entrepreneurial risk for mining operators themselves (Bergeron et al., 2015).

Therefore, a mining project needs to guarantee optimal socio-ecological performances in order to sustain as well its business dimension. This need has been translated into specific frameworks developed over the years such as Social Corporate Responsibility and Social License to Operate (see Chapter 1). These frameworks might – at least from a theoretical point of view – might support the realization of the sustainable development goals (SDGs). For instance, the non-acceptability of a mining project by society cannot be considered as a “suffered” consequence *per se*, but rather as a social response to the positive and negative risks that a mine might generate or that are expected to be generated. Social acceptability is not part of a risk scenario, but it is influenced by it since it might vary according to the score of the TMS.

The improvement of mining practices found a way also through the concept of “responsible” mining (IRMA, 2014). Mining responsibility is social, political, environmental and economic, and it can find operationalization through the integration of new safety measures or new *green* technologies into mining. These initiatives can also take organizational forms. For instance, as processing methods can be complicated and require great capital costs and skills, some countries are trying to develop centralized centres where artisanal miners can process their ore. These “Amalgamation Centers” or “Processing Centers” would allow a better control from authorities, a significant performance for miners and greater acceptability from local communities (Hinton et al., 2003).

Hence, further improvements of our approach should focus on the combination of project-scale risk assessment methods and decision-making tools at the territory level the
integration of the risks suffered by mining activities. This implies the inclusion of both the
entrepreneurial dimension of each mine-type and the transdisciplinary analysis of the social
context where mining is performed and the risk of potential contestation.

3.4. *Integration of decision-making tools*

The approach we propose in this study serves the purpose to support decision-making in land-planning processes of mining regions. Rather than directly giving the decision, which is out of the scope of a scientific research, our approach allows to structure the territorial objectives and the related alternative strategies for land-planning. The TMS represent these strategies and they can be compared based on their risk score. Hence, ideally, public authorities should select, interpret and discuss the best suitable option(s) for a further implementation.

The proposed approach should be improved through the integration of well recognized multi-criteria decision-making methods such as Analytical Hierarchy Process (AHP), ElectrIII, TOPSIS, etc. This would allow a more symmetric integration of the negative and positive risks for each scenario through structured approaches. At this stage, the proposed approach is based in a certain way to the AHP, which has the purpose to answer to a general question (i.e. which is the best mining strategy?) offering a series of alternatives (i.e. the TMS) based on multiple criteria (i.e. the RSa and RSo scores) (Fig. 15).

Also, as already mentioned in the previous Chapter, LCA studies can improve the
assessment of the interactions between the mining system and the territory system. For
instance, recent studies about Territorial Life Cycle Assessment (Loiseau *et al.*, 2018) could be
helpful to ameliorate the approach and quantify, for example, the *socio-eco-efficiency* of mining
projects, defined as a ratio of the negative and positive risks related to a mine. The
improvements concern as well the development of weighting coefficients. A method that can
be applied is, for instance, the Saaty Scale (Saaty, 2008) which is widely used, especially with
AHP, to generate priorities and make an actual decision according to stakeholders' perception.

3.5. *The development of an automated GIS-based tool*

The operationalization of the approach proposed in this study has been concretely
performed through a series of separated and time-consuming tasks in order to acquire and
process the data used to obtain the final TMS scores. For instance, some data have been
repeatedly geoprocessed on GIS-based softwares, particularly for the localization of the mines
and the estimation of the RSa score. Since the tasks for the geoprocessing and assessment of
the risk scores and vulnerability are performed individually this might increase the occurrence
of human errors and be excessively time-consuming for a rapid application.

Therefore, a significant perspective should be the development of automatized GIS-
based tools to allow a faster, simpler and more operational application of the approach and its
methodology. The automatization of GIS-based tools is widely discussed by scientific literature (Jebamalai et al., 2017; Nordin, 2017; Cao et al., 2019; Rahmati et al., 2019). For instance, Bhattacharya et al., (2014) developed an automated tool for natural hazard zonation mapping. These kinds of studies might apply as well to the fictional siting of mining projects, without requiring for the modeler to manually locate each mining project-types for each scenario. GIS-automation can be developed through the translation of the current individual tasks into programming codes using multiple languages (e.g. C, C++, Java, Python). This can lead also to the development of interactive web-GIS tools (Aye et al., 2016) that would be an interesting tool to enhance stakeholder's involvement. Therefore, such improvements would ameliorate the operationalization of the approach, its easier application and reducing processing time. They also could include uncertainty analysis and the decision-making methods previously described. However, it is important to underline that a potential automated tool (or software) should always leave the place to adaptations on a case-to-case basis and to integrate the critical analysis and discussion of the potential results.
General Conclusion

The main ratio of this thesis, emphasized by a growing scientific literature over these last decades, is the integration of mining into wider territorial approaches. The current thesis does not question whether mining is necessary or not, whether the industrial applications of specific commodities – and thus, their mineral exploitation – are justified or not, neither whether a given mine should be developed at a given place or not. If mining exploitation is necessary for a given commodity, technological and socio-economic development should never compromise and undermine socio-ecological integrity.

The socio-ecological dilemma does not consist in the opposition or “mining vs not mining” and cannot be resolved through a strict boolean ultimatum. Mining is globally driven by a growing demand for minerals and metals. Therefore, the main challenges are related to understanding and improving how mining is performed and, before that, the possibility of supplying raw materials at least partly through other complementary methods (e.g. recycling, agromining...or else?), or to reduce the demand to some extent.

For such reasons, our study aims to highlight the potential positive and negative contributions of mining at the territorial level, overcoming the simple perimeter of the mine site itself. Mining projects are a matter of land-planning rather than localized industrial objects. Hence, territorial management and land-planning of mining territories should be supported by tools that allow to propose and foresight potential development strategies and analyze them based on different criteria: e.g. the localization of the mine site, the level of positive and negative risks, the conflictual nature of mining projects.

It has been said that the engagement of mining into land management relies on multiple factors that involve land-use, public policies and market trends. Therefore, if and how the development of the mining sector should take place in a given territory implies political answers and political objectives. This implies a distinction between the decision-maker, citizen and the scientific researcher. In any case, the roles of these distinctive actors are significantly complementary and must be effectively taken into account when it comes to land-planning.

As researchers, in this thesis we presented the first development of an original systemic approach that compares potential land-planning scenarios for mining territories based on their level of risk. In view of the advantages and the gaps of specific risk assessment methodologies, the framework used for the application of our approach is intended to provide a structured and pluridisciplinary analysis that acknowledges both the specificities of mining projects and the socio-ecological vulnerability of the territories in which these activities are located. Because of its territorial resolution and its operational purposes, our approach and its application have proven to be adapted to the level of available data, including the involvement of stakeholders'
perception and cartographic outputs to support sharing and communication of the results and
a better integration within decision-making tools.

The demonstrative application here presented focuses on gold mining in French Guiana, a French Overseas Territory located in northern South America. The results of our approach are represented by global scores given to each territorial mining scenario. The rendering of such results has been spatialized, through a GIS-based approach and under the form of sheets. In both cases, the shareability and communicability of such results is very accessible for local stakeholders and allow to have an overall value for each scenario, facilitating the comparison and discussion of these strategies and supporting decision-making. The synthetic process that leads, from a complex risk scenario (RSo or RSa), to one simple score, might imply a considerable loss of data and information. However, the transparency of the methodology allows to scan and go through each phase of the assessment process in order to analyze each used data and the obtained results. The underestimation or overestimation, uncertainty and variability of results are driven by how the method is performed and on which data is based. Despite future challenges must improve the reliability of the results, the objectives in this thesis were to show the feasibility of our approach and its rapid applicability.

Furthermore, our study was open to the identification of multiple future leads, that have been highlighted in the last chapters of the manuscript. As mentioned previously, the current thesis represents the first step of a wider research objective that aims to apprehend the socio-ecological footprint of mining at the territory level. Therefore, this studied was intended to identify as well the challenges to address in the future to improve the effectiveness of our approach and its application. Further researches for the improvement of our approach should focus on multiple challenges that concern two complementary aspects.

i) First, the current application of our approach to the FG case, for which further studies should:

a. Consider the level of **uncertainty** of the final scores based on the data used, which would guarantee more reliable comparisons of the different TMS developed. This implies as well the estimation of the level of **reliability of the input data**, their **processing** and of the **final results**, in order to better operationalize our approach;

b. Develop and compare SMT based on **multiple territorial objectives** at the same time rather than only one, as it is the case in the current feasibility test of the proposed approach.

c. Improve the development and the risk assessment of the TMS during normal operating conditions (RSo), through the integration of more risk events, the analysis of their chain of cause-consequences and the estimation of the corresponding risk
level based on the identification of impacted areas and socio-ecological
vulnerability;

d. Collect data concerning the **safety measures** that might reduce the probability of occurrence of the dam failure or its consequences. These safety measures must be quantified and integrated within the final global TMS scores, for instance, through approaches such as the Bow-Tie, which can be a performant assessing tool, although it allows the analysis of only one failure at a time;

e. Extend the current assessment to **new TMS** (see Table 13) and other risk scenarios. The dam failure was chosen as central accidental risk event based on two risk databases (Chapter 6). New applications might consider explosions, spills, transport accidents;

f. Improve the **weighting** of the final TMS scores through new surveys allowing the homogeneous integration of all the stakeholders groups considered in this study (e.g. civil society, public administration, mining operators);

g. Deepen the understanding of the socio-ecological vulnerability of the territory where mining activities are located, for instance, improving the **quantification of the ecosystem services** delivered by a territory and the potential impacts that mining can have on such services and the related societal needs. Several frameworks and indicator-based approach exist for the assessment and mapping of ecosystem services and the corresponding societal needs (Berrouet et al., 2018);

h. Improve the quantification of **cumulative risks** of multiple mining projects coexisting in a given territory. Land-planning strategies must apprehend if the cumulative risks of dozens of small mines can outweigh the risks generated by one large mine. This imply systemic approaches such as territorial LCA, and a better involvement of stakeholders;

i. Improve the **quality and quantity of data** used for the assessment, integrating more precise information gathered specifically for such purposes and allowing a more detailed modeling and assessment). For instance, socio-ecological vulnerability might integrate quantitative data related to soil and water resources, drinking water availability, health vulnerability but also more specificities concerning local population and the characteristics of private and public infrastructures (e.g. construction materials for roads and buildings, year of construction, monitoring). Further information concern as well gold mine-types in order to enrich and complete the list of their features (Appendix 5);
ii) Secondly, further studies must improve <u>the general methodological framework</u> that would ensure the reliable applicability of our approach to other case-studies, serving decision-making purposes. These are related to:

a. A more structured and quantifiable integration of all mining **stakeholders** during the entire application of the approach, from the definition of the territorial objectives, to risk identification, risk weighting and TMS comparison;

b. The effective integration of **decision-making tools** (e.g. ANP, AHP, TOPSIS, ElectrIII, multi-agent system approaches) to ensure the operationalization of our approach and the ranking of the TMS to better serve decision-making purposes;

c. The implementation of our approach within an **automated GIS-based tool** that should be developed to guarantee a more rapid application of the approach and its suitability for decision-making;

d. The integration of industrial risk management methods to perform also risk assessment at the project-level to quantify the **rebound dynamics** (i.e. from the territory to the mining project) that affect the performance of a mining project. This implies as well the mobilization of social and human sciences to analyze and quantify the patterns that lead to entrepreneurial risks driven by the contestation and opposition of civil society to mining activities;

e. Finally, the possible integration in the future, of complementary approaches that focus on **other human activities** (e.g. forestry and silviculture, agricultural activities, urban development, industrial plants) and their interactions with mining at the territorial scale. This might allow to extend the application of our approach to holistically serve land-planning decision-making not only in relation with the mining sector.
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