sustainable mining practices: the role of professional associations and researchers
TRANSCRIPT
Sustainable Mining Practices: The Role of Professional Associations and Researchers
Kwame Awuah-Offei, PhD, PEAssociate Professor of Mining Engineering
> In the last 30 years, mining companies & their stakeholders have embraced sustainable development practices & concepts
> Most top mining companies now recognize the need for more than environmental compliance
Mining & Sustainability
Environmental compliance
ISO 1400 EMS
Corporate Social Responsibility
Sustainability
Sustainability means many things
ICMM Principles1 Apply ethical business practices and
sound systems of corporate governance and transparency to support sustainable development
6 Pursue continual improvement in environmental performance issues, such as water stewardship, energy use and climate change
2 Integrate sustainable development in corporate strategy and decision-making processes
7 Contribute to the conservation of biodiversity and integrated approaches to land-use planning
3 Respect human rights and the interests, cultures, customs and values of employees and communities affected by our activities
8 Facilitate and support the knowledge-base and systems for responsible design, use, re-use, recycling and disposal of products containing metals and minerals
4 Implement effective risk-management strategies and systems based on sound science and which account for stakeholder perceptions of risks
9 Pursue continual improvement in social performance and contribute to the social, economic and institutional development of host countries and communities
5 Pursue continual improvement in health and safety performance with the ultimate goal of zero harm
10 Proactively engage key stakeholders on sustainable development challenges and opportunities in an open and transparent manner. Effectively report and independently verify progress and performance
Sustainable resource development contributes to SD goals
“Yet today, a paradox seems to be emerging. In spite of improved performance, mining operation-community conflict appears to be on the rise. While the number of mines is similarly increasing (Miller, 2014; personal communication) across the world, it is not clear what is behind this apparent paradox.”
– Hodge, R. A. (2014). Mining company performance and community conflict: moving beyond a seeming paradox. Journal of Cleaner Production, 84, 27-33.
Challenges still exist
Source: Mathews et al. (2004)
> Develop standards of practice for their members based on the best science and our current understanding of the SD impacts of mining
> Develop codes of ethics and enforce these for their members
> Disseminate information on the best science through conferences and journals
> Facilitate continued discussion and research
Professional Associations should…
Ethics
PREAMBLE: Members of The American Institute of Professional Geologists are dedicated to the highest standards of personal integrity and professional conduct. The Institute’s Code of Ethics comprises three parts: the Canons, which are broad principles of conduct; the Ethical Standards, which are goals to which Members aspire; and the Rules of Conduct. Compliance with the Rules of Conduct is mandatory and violation of any Rule will be grounds for disciplinary action by the Institute. Under the Bylaws, the Institute may also impose discipline for legal violations and because of the suspension or revocation of registration or licensure, among other grounds. Disciplinary action may take the form of private admonition, public reprimand, suspension of membership, or termination. The Code of Ethics applies to all professional activities of Members and Adjuncts, wherever and whenever they occur. The title “Member” where used in this Code of Ethics shall include Adjuncts. A Member shall not be relieved of an ethical responsibility by virtue of his or her employment, because the Member has delegated an assignment to a subordinate, or because the Member was not involved in performing services for compensation.
AIPG Code of Ethics
> Explore questions to increase our understanding of the sustainable development impacts of mining
> Develop SD metrics that are more relevant to evaluating SD impacts of mining
> Develop & disseminate more sustainable mining practices
Researchers should…
> LCA is a useful technique for evaluating cradle to grave impacts
> Limited (but improving) mining data sets> Databases are black boxes when it comes to
mining impacts
Life Cycle Assessment
© 2017 - Marcel Gómez Consultoria Ambiental.
> Economies of scale have significant effect on life cycle impacts
> Geologic factors are significant– Coalbed methane content– Coal quality– Stripping ratios
Effect of mine characteristics
0.01 0.1 1 10 1000
20
40
60
80
100
Log of annual production (million tons)
Clim
ate
chan
ge im
pact
(kg
CO
2-eq
)
Ditsele, O., & Awuah-Offei, K. (2012). Effect of mine characteristics on life cycle impacts of US surface coal mining. The International Journal of Life Cycle Assessment, 17(3), 287-294.
> Elevated CO2 concentrations in homes is now being recognized as a safety & health hazard– 4 fatalities in the literature– [CO2] > 25% (STEL = 0.5%)– [O2] < 10% (STEL = 19.5%)
> Several cases reported in several states (OH, PA, WV, IN), UK, & Canada.
> Source = AMD-carbonate neutralization
CO2 Hazards on Reclaimed Mine Land
2( ) 3( ) ( ) 2 ( ) 2( )2 aq s s l gH CaCO Ca H O CO
CA Trace Gas Monitoring
Chiodini, G., Caliro, S., Cardellini, C., Avino, R., Granieri, D., & Schmidt, A. (2008). Carbon isotopic composition of soil CO 2 efflux, a powerful method to discriminate different sources feeding soil CO 2 degassing in volcanic-hydrothermal areas. Earth and Planetary Science Letters, 274(3), 372-379.
> Godin Site, Sommerset, PA> Hudson Site, IN> Germantown, MO
Study sites & sampling plans
We showed that CO2 fluxes on reclaimed mine land is sometimes spatially correlated
Spatial CorrelationStudy site
Data set No. of samples
Global Moran’s I
Expected value
Std. Deviation
Z value p-value
Hudson
Day 1 131 0.702 - 0.00763 0.0834 8.45 < 0.0001
Day 2 136 0.528 - 0.00741 0.084 6.42 <0.0001
Day 3 131 0.475 -0.00763 0.0922 5.24 <0.0001
Godin
Day 1 71 -0.198 -0.0143 0.201 -0.912 0.3618
Day 2 71 0.098 -0.0143 0.202 0.555 0.5772Day 3 71 0.241 -0.0143 0.208 1.228 0.2193
German-town
Day 1 40 -0.106 -0.0256 0.489 -0.1652 0.8688Day 2 88 -0.093 -0.0115 0.984 -0.0829 0.9339
Day 3 98 0.154 -0.0103 0.0536 3.070 0.0021
Mathiba, M., & Awuah-Offei, K. (2015). Spatial autocorrelation of soil CO2 fluxes on reclaimed mine land. Environmental Earth Sciences, 73(12), 8287-8297
Hazard Delineation
• Awuah-Offei, K., Mathiba, M., & Baldassare, F. J. (2016). Identifying the Presence of AMD-Derived Soil CO2 in Field Investigations Using Isotope Ratios. Minerals, 6(1), 18.
• Awuah-Offei, K., Que, S., & Mathiba, M. (2016). Delineating hazardous CO2 fluxes from acid mine drainage. Environmental Earth Sciences, 75(3), 1-11.
> Developing mineral resources for sustainability depends on:– Community perceptions of the project– The interaction between the project and the community
Community-Mine Interactions
> Community engagement & perceptions linked to social license to operate & socio-political risks
> Engineers, often, design development alternatives based on trade-offs between different impacts, without fully understanding community members’ preferences
> We need better understanding of these preferences in order to design alternatives that are more likely to meet host community aspirations.
> No formalized objective framework exists for evaluating these questions over time.
Problem Statement
Objective & Approach
Main conceptsGoal
To improve understanding of the relationship
between sustainability of mining projects
and their community
acceptance.
ABM
Discrete Choice Theory
Social Networks
> Based on random utility maximization> Decision maker’s overall preference for an
alternative is a function of the utility
Discrete Choice Theory
Tni ni ni ni U β x ε
Case Study: Salt Lake City, UT, USANo. of
participants Group 1 Group 2 Group 3 Total
Invited 755 669 >386 >1,810 Started 485 316 386 1,062Completed (i.e. answered all question to the end)
300 261 261 882
Terminated by quality control question or survey duration
12 10 22 44
Excluded due to demographic factors
74 40 36 150
Final qualified 214 211 203 628
Demographics Sample SLCGender Male 47% 50%Female 53% 50%Age18—25 9% 18%26—34 28% 26%35—54 34% 31%55—64 16% 12%>65 13% 13%Highest education<high school 0% 14%High school/GED 10% 18%Some college vocational, or 2 year college degree 34% 27%>Bachelor's degree 56% 41%Annual income< $20,000 7% 22%$20,000—$39,999 21% 23%$40,000—$59,999 22% 18% =>$60,000 51% 37%
> 21,600 data: 3 groups × 200 subjects (minimum) ×4 blocks × 3 choice sets × 3 alternatives
Modeling ResultsParameter Coefficient Parameter CoefficientIntercept 0.8931** Economic Social Job opportunities 1.1259*** Population increase -0.0709Income increase 0.6600*** Infrastructure improvement 0.6527***Increase in housing costs -1.0416*** Crime increase -1.1753***Labor shortage for other business
-0.0924** Traffic increase -0.1938***
Environmental Governance and other Noise pollution -0.9580*** Decision making
mechanism0.1634***
Water pollution -0.1956*** Information available 0.8460***Air pollution -1.0952*** Mine buffer 0.6684***Land pollution -0.2485*** Mine life 0.1181***Demographic factors ***1% significance levelAge 0.0100** **5% significance levelGender -0.0200* *10% significance levelHousehold income 0.0043*Education 0.0013*
> An approach to modeling complex-adaptive systems using multiple agents, that interact based on “simple” rules, to capture overall system behavior
> Key elements– Agents– Agent interaction
(topology)– Environment
Agent-based Modeling
27
Agent Interactions with Other Agents
Agent Interactions with the Environment
Agent Attributes: Static: name, gender… Dynamic: memory, resources
Methods: Behaviors Behaviors that modify behaviors Update rules for dynamic attributes
> Discrete choice model can serve as utility function
> We chose to use the odds ratio based on the utility function
Agent Utility Function
Tni ni ni ni U β X ε
Ta b
abOR e β x x
> Agent’s interact through a social network (network topology)
> Agent’s exchange information on their perceptions of the mine’s attributes
> New perceptions “diffuse” through the network from agent to agent
Agent Interaction …over time
> We used the Bass Model for information diffusion> We have implemented two kinds of diffusion
models– With and without agent innovation– With and without social value to adopt innovation
Diffusion Models
1dF t
p qF t F tdt
1 d
d dv
> For the model with innovation but without aging etc., we examined three major parameters (p, q and average degree of the network)
> Sensitivity assessed using variance decomposition methods1
> First order and total effects sensitivity indices estimated using 1,024 replications
Sensitivity Analysis
Parameter DistributionProbability of innovation, p Uniform [0.01, 0.07]Probability of imitation, q Uniform [0.0005, 0.01]Degree of social network Uniform [5, 30]
1 Saltelli A, Ratto M, Andres T, et al (2008) Global Sensitivity Analysis. The Primer.
> Research and appropriate technology transfer to facilitate safe & environmentally responsible artisanal mining
> How does mining contribute to the cyclical economy?> Tailings and the risks they pose to the environment> How to create growth media on tailings to facilitate
revegetation using native species> Acid mine drainage continues to be a huge problem> Passive and phytoremediation using native species
Many other research questions remain
> The mining industry has made significant progress in the last 30 years or so
> Societal expectations have also increased in that space> Mining has and continues to contribute to development> Professional societies like SODOGEO have a role to play to
ensure mines of the future are more sustainable> Contextual research is necessary to ensure mining
contributes to SD
Summary
