1. Introduction
Engineering design is a process that includes: (i) establishing design objectives and specifications; (ii) determining measures of performance; (iii) generating design alternatives; (iv) evaluating and testing alternatives; (v) selecting the optimal alternative; and (vi) implementing the best design. Mine planning and design, which is the process of devising a plan for developing a mineral deposit (i.e., preparing the land for extraction), extracting the deposit, and reclaiming and closing the mine, follows a similar process. The nature and objectives of mine planning and design vary depending on the scope (strategic versus tactical) [
1]. Generally though, the objectives can be categorized into economic (e.g., maximizing returns, reducing costs, etc.), safety (e.g., ensuring safe equipment interaction, ensuring stable openings and slopes, etc.), and sustainability objectives (to the extent that one can separate worker safety and health from broader sustainability goals, e.g., reducing biodiversity impacts, reducing emissions to air and water, reducing poverty in the host community, etc.) [
2].
While mining engineers have many tools for evaluating economic [
3] and safety [
4] objectives, there are not as many tools for evaluating sustainability considerations in mine planning and design [
5,
6,
7]. Yet, especially in long range mine planning, there are many decisions that have significant sustainability implications and impact the social risks associated with mineral projects. (We define social risks here to mean “the range of potential impacts on a project that may result from its interaction with communities and stakeholders”, per Barclay et al. [
8]. We note that Kemp et al. [
9] call this type of risk “business risk” to differentiate it from risk to communities. For our purposes, we choose to call it “social risk” to differentiate this risk from other risks to the mining project.) As evidenced by recent large projects (e.g. Pascua Lama and Bristol Bay) that have been halted or significantly altered because of community concerns over how the mine plan will impact the environment and ecological services, the sustainability considerations carry significant implications for project success. Thus, the ability of mining companies and mine engineers to adequately address sustainability in design hinges on the availability of rigorous tools that facilitate sustainability considerations in addition to other efforts (e.g., making mine planning teams more inter-disciplinary so that they are better able to identify and quantify social risks).
In the last few decades, ideas regarding how we incorporate environmental aspects into design have evolved into eco-design and, ultimately, to the concept of design for sustainability (DfS). DfS is more comprehensive and far reaching in its aspirations to re-imagine how to satisfy the need while considering long term sustainability impacts. For mining to contribute to sustainable development, the industry will have to adopt more DfS approaches and principles in the way it does business. This is even more important given the material intensity of the green energy transition [
10].
Rocha, Antunes, and Partidário [
11] describe a DfS hierarchy that moves from higher level strategic activities to tactical and to operational activities with narrower scopes. The mining industry would have to engage in DfS at all these levels to align its business practices with sustainable development goals. Mine planning and design fits into the operational level as it involves designing how a company goes about extracting resources. Nonetheless, it is still important for the industry to conduct mine planning and design with sustainability in mind. To fit into the DfS paradigm, mining engineers need rigorous tools to evaluate the social and environmental aspects of the design.
A key element of accounting for sustainability in mine planning and design is incorporating the preferences of individuals in host communities and other stakeholders. This has been formalized into concepts such as free, prior, and informed consent [
12,
13,
14], social license to operate [
15,
16], and legitimacy [
16,
17,
18]. Yet, the literature lacks formal frameworks for design engineers to incorporate the preferences of host communities into their design. The few examples that exist (e.g., [
5]) are based on multi-criteria decision analysis tools that rank alternatives based on subjective weights [
19,
20]. Thus, in many cases, engineers design the mine without due regard to community preferences and the companies try to "sell" the "optimal" design to the community and other stakeholders leading to conflict and delays in mine permitting. In this paper, we argue that just as decision-based design [
21] has been used to formally incorporate customer demand into product design [
22], it can be used, in combination with discrete choice analysis, to incorporate community acceptance (preferences) in long range mine planning and design. When done well, the decision-based design approach should avoid subjective weights and, thus, overcome the weakness of multi-criteria decision analysis approaches. This paper, therefore, seeks to present a framework based on decision-based design and discrete choice analysis for incorporating local community acceptance into mine planning and design. We focus on long range mine planning and design because that is the stage where mining engineers make strategic decisions with significant impact on sustainability [
1].
3. Case Study
To illustrate our approach, we use a case study of the strategic mine planning of a gold deposit (
Figure 3).
Table 2 shows the base case input parameters used in the strategic mine planning exercise conducted in GEOVIA Whittle. The case study is an open pit mine, which we assume will be mined with conventional truck and shovel mining, heap leaching of the crushed ore followed by carbon in column (CIC) processing to recover gold. We used Whittle to determine the optimal pit outline using the pseudoflow algorithm [
36] and evaluate various scheduling options (
Figure 3). We created design alternatives using initial capital, mining production rates, processing throughputs, and annual production costs (which are a function of the unit cost and annual production) as the design variables.
Table 3 shows these design variables. These design variables were correlated with project attributes that influence individual preferences. We used all 16 project attributes that we found to be relevant in our previous work [
29]. However, the 10 alternatives we evaluated in this work vary only in the number of employees (job opportunities) and mine life (
Table 3). All other attributes from our previous work are deemed to remain the same for these alternatives.
We wrote a MATLAB code with the following pseudo code to simulate the case study and estimate the utility.
READ input data
FOR number of replications
Sample demographic characteristics
Estimate for each individual n and alternative i (Equation (2))
Determine unit social risk cost using Equation (3)
Estimate average unit social risk cost
Estimate present value of total social risk cost
After generating the design alternatives in Whittle, we extracted the data from Whittle and used them as input into our MATLAB code to estimate the utility after accounting for social risk costs. The key steps of the process are explained in the following subsections. For this case study, we run 1000 replications to examine the uncertainty.
3.1. Community Preferences
We used a conditional logit model (a discrete choice model) of a mining project similar to the one in this case study, from our previous work [
29], to model individual preferences for the alternatives.
Table 4 shows the coefficients for all variables in the model, which was for the mining community of Salt Lake City, Utah, USA. We used Equation (2) to estimate the probability that each simulated individual in the community will choose each of the 10 alternatives. We simulated 50,000 residents using the same demographic distributions for St. Lake City, UT, USA, for age, gender, education, and income as we previously reported [
29]. We used Monte Carlo simulation to sample from the demographic distributions.
3.2. Cost
Our optimization model in Whittle already contains mining and processing costs using the unit costs in
Table 2. We modeled the social risks as additional cost that depends on the average probability of residents choosing that alternative. The relationship between the probability of acceptance and social risk cost depends on the project and will require empirical data to fit an accurate curve. In this case study, we use a unit cost per tonne approach to account for scale of production. A quadratic function (e.g., Equation (3)) with a stochastic term,
, can adequately express such a relationship between the probability of acceptance and the unit cost (per tonne of mining) of social risk,
. For this particular case study, we used
a = 0.5,
b = −1, and
c = 05 (
Figure 4) and a normal distribution with mean of zero and standard deviation of 0.1 for the stochastic term. We used this primarily for illustration, but this function meets all the properties defined in
Section 2.2.3.
3.3. Utility
The utility function depends on the company’s preferences and how they value the various benefits and costs. We use the expected net present value (NPV) of cash flows as the utility as the NPV is a common metric for evaluating mine planning and design alternatives. We use the discounted cash flows from our Whittle optimization work, combined with the discounted social risk costs from the Monte Carlo simulation to estimate the NPV.
3.4. Uncertainty Characterization
We use Monte Carlo simulation to characterize uncertainty in our case study. While there are several stochastic variables in this problem, we chose to focus only on the uncertainty in the estimate of the cost associated with social risk because: (1) the literature contains many examples of work related to characterizing uncertainty in mine planning and design; (2) accounting for the social risk and uncertainty is the main focus of this work; and (3) we believe there is no loss of generality as one can easily use Monte Carlo simulation to account for uncertainty due to other variables once their distributions are known.
In this case study, the main sources of uncertainty are the demographics of the simulated population and its effect on the probability of accepting the design alternatives, and the uncertainty in the unit cost (Equation (3)). In the case of the demographics, we sample the population demographics from the cumulative density functions constructed from the distributions of age, gender, education, and income. For the unit cost, we account for uncertainty by sampling for the stochastic term, ε, from the normal distribution.
3.5. Results
Figure 5,
Figure 6 and
Figure 7 and
Table 5 show the results of the case study. The
Appendix also shows the optimal production sequence for all alternatives from Whittle.
Figure 5 shows the distribution of unit costs for Alternative 1, as an example. As one would expect, given that we assumed the stochastic term is normally distributed, the distribution is symmetric. We observe similar trends for the other alternatives.
Figure 6 shows the social risk cost per tonne of mining from the simulation as box plots. When that cost is applied to the total amount of material mined each year and discounted, we obtain the total present value of cost associated with the social risk for each alternative (
Figure 7). That overall cost ranges from
$36.4 to
$45.5 million for this case study.
Table 5 shows the expected NPV for each alternative after accounting for the social risk costs in the evaluation. The overall NPVs range from
$3.553 to
$4.256 billion.
3.6. Discussion
Based on the case study results in
Table 5, Alternative 10 is the optimal alternative as it has the highest expected NPV (utility) of
$4.256 billion. While this happens to also be the alternative with the highest NPV without accounting for community acceptance and attendant social risk costs, the benefit of using the approach proposed here is still clear. The decision-based design framework we propose here explicitly accounts for the community acceptance and adjusts management’s expectation of value (
$4.256 instead of
$4.301 billion). Moreover, in cases where the cost of social risks is high, relative to the overall NPV, it can be a determining factor in evaluating alternatives. In this case study, however, the magnitude of the social risk cost is not large enough to change the outcome of this analysis. It is important to note, though, that this is not always going to be the case.
The social risk cost, in this case, is affected more by employment opportunities associated with the alternatives than by mine life.
Figure 8 shows the relationship between the social risk cost and employment opportunities. The observed relationship is consistent with the discrete choice model we used in our case study.
Table 4 shows the coefficients of the conditional logit model for job opportunities (number of people to be employed) and mine life are 1.1259 and 0.1181, respectively. It is an interesting observation because, mine life affects the present value of the social risk cost beyond its influence on the unit cost of social risk (through its influence on the community acceptance). The mine life affects the number of years the project incurs social risk cost in estimating the present value of the cost. Nevertheless, in this case, the employment opportunities significantly affect the overall social risk cost and should drive design decisions.
Overall, the case study illustrates that we can successfully incorporate community acceptance and the associated social risks cost into long range mine planning and design using decision-based design and discrete choice theory. As we mentioned earlier, there are only limited examples of work that explicitly incorporates sustainability factors into mine planning and design beyond adding the cost of environmental remediation into the cost estimate. For example, Adibi and colleagues [
5] incorporated a list of “sustainability indicators” into the ultimate pit limits optimization process for open pit planning using multi-attribute decision making tools. Their approach, however, relies on quantitative multi-criteria decision making, which as others have noted, often masks the subjective decisions inherent in accounting for qualitative criteria [
19,
20]. Our proposed framework explicitly accounted for community acceptance and its associated social risk costs, which is normally an externality, using decision-based design and discrete choice theory. By proposing a means to estimate the cost accruing from a lack of community support with attributes of the design alternatives, we avoid the use of subjective weights to evaluate the importance of community attributes in the design. This is the key contribution of this work.
We recognize that, while tools that help engineers quantify the nature and costs of risks arising from interactions with stakeholders can be helpful in gaining the attention of decision makers inside companies, these tools alone cannot ensure community acceptance. Therefore, using the tools proposed in this paper, by themselves, do not ensure that host communities will accept the optimal mine plan generated by engineers. The use of this and similar tools will only be successful if they are combined with a genuine effort to create shared value and effective community engagement to understand the concerns and desires of community members and other stakeholders. If the design objectives are jointly formulated (or at least formulated with input from stakeholders), then designs that drive towards those objectives are much more likely to receive acceptance from the community members.
We believe further research can incorporate other sustainability factors into the framework proposed by this work to make for a more comprehensive evaluation. For example, with the push towards low carbon mining, one could incorporate the cost associated with carbon emissions into this framework easily. Other factors such as the impact of the mine on ecological attributes and the ecological services they provide to the community can also be incorporated if cost functions are developed explicitly to account for the associated costs and benefits associated with mine planning and design alternatives. There is basis in the literature for assigning value to ecosystem services provided by nature [
37] that are promising for this type of work. Future work should attempt to incorporate these into the framework proposed in this work.
4. Conclusions
This paper presented a framework based on decision-based design and discrete choice analysis to account for local acceptance in mine planning and design. The framework builds on previous work on decision-based design that uses discrete choice analysis to evaluate customer demand for the design alternatives. The proposed framework uses discrete choice analysis to estimate the probability that the community will accept each design alternative. Using a cost function that relates the level of community acceptance (probability of community members choosing that alternative) to social risk cost, this framework incorporates community acceptance as a cost term in estimating the expected net present value, which is the utility for the design decision.
The work uses the mine strategic mine planning exercise of a gold mine as a case study to illustrate this framework. For the specific case study, Alternative 10 is the optimal alternative with an NPV of $4.256 billion after accounting for social risk cost over the life of the project that amounts to $45 million in present value terms. The work finds that the social risk cost is more sensitive to the job opportunities associated with the alternatives than the mine life. The case study shows that the proposed framework is viable and can adequately incorporate community acceptance. This framework can be a useful tool for designing mines for sustainability, if combined with effective community engagement and management’s desire to create shared value.
Future work should extend this framework to include other sustainability factors such as carbon footprint and ecosystem services impacted by mining. The basic framework of decision-based design and discrete choice analysis can be extended to incorporate these terms if functions can be developed to incorporate these items as costs or revenues.