MineSDG formalises the relationship between the 17 SDGs and
mining-sector materiality as a queryable dataset,
sdg_mining_ontology. Each goal is mapped to a mining
domain, a 1-5 materiality rating, a material topic, and references into
GRI 11 (the 2024 mining sector standard), the ICMM Mining Principles,
SASB EM-MM metrics and SEBI BRSR principles.
explore_sdg_ontology(goal = 6)
#> goal goal_name mining_domain materiality
#> 6 6 Clean Water and Sanitation Water & Resource Efficiency 5
#> material_topic gri_reference
#> 6 Water stewardship, quality and shared-use catchments GRI 11.6 / 303
#> icmm_principle sasb_emm brsr_principle
#> 6 Principle 6 EM-MM-140a P6
#> example_kpis
#> 6 water_recycling_rate; net_water_consumption
explore_sdg_ontology(domain = "biodiversity")[, c("goal", "material_topic",
"gri_reference")]
#> goal material_topic gri_reference
#> 14 14 Marine and coastal impacts (tailings, ports) GRI 304 (coastal)
#> 15 15 Land disturbance, rehabilitation and biodiversity GRI 11.7 / 304The five core-materiality goals for mining (rating 5) are health & safety (SDG 3), water (SDG 6), climate (SDG 13) and land/biodiversity (SDG 15) — consistent with how ICMM members and GRI 11 frame sector materiality.
sdg_mining_ontology[sdg_mining_ontology$materiality == 5,
c("goal", "goal_name", "mining_domain")]
#> goal goal_name mining_domain
#> 3 3 Good Health and Well-being Health & Safety
#> 6 6 Clean Water and Sanitation Water & Resource Efficiency
#> 13 13 Climate Action Climate & Energy
#> 15 15 Life on Land Biodiversity & Landmining_kpi_registry defines 18 site KPIs with units, SDG
targets, improvement direction, and indicative good /
poor reference thresholds that anchor 0-100 scoring:
list_mining_kpis(sdg_goal = 8)
#> kpi_id kpi_name unit
#> 7 trifr Total Recordable Injury Frequency Rate per 1M hours
#> 8 ltifr Lost Time Injury Frequency Rate per 1M hours
#> 9 fatality_rate Fatality Frequency Rate per 1M hours
#> 11 local_employment_pct Local Employment Share %
#> 17 local_procurement_pct Local Procurement Share %
#> sdg_goal sdg_target direction good_value poor_value
#> 7 8 8.8 lower_better 1.5 12.00
#> 8 8 8.8 lower_better 0.4 4.00
#> 9 8 8.8 lower_better 0.0 0.05
#> 11 8 8.5 higher_better 80.0 20.00
#> 17 8 8.3 higher_better 60.0 10.00
#> framework_reference
#> 7 GRI 403-9; ICMM safety data convention (per 1M hrs)
#> 8 GRI 403-9; ICMM safety data convention
#> 9 GRI 403-9; ICMM safety data convention
#> 11 GRI 202-2; SASB EM-MM-210b
#> 17 GRI 204-1; ICMM Principle 9Calibration note. The bundled thresholds are indicative sector reference points. For production use, copy the registry and calibrate
good_value/poor_valueto your commodity, scale and jurisdiction, then pass your version toscore_site_sdg(registry = ...).
score_site_sdg() takes one site-year of raw operational
data, derives every KPI it can, rescales each between the registry
thresholds (respecting direction), aggregates to SDG-goal level, and
weights goals by ontology materiality into a composite:
site <- demo_mine_sites[demo_mine_sites$site_id == "FE-PILB" &
demo_mine_sites$year == 2024, ]
result <- score_site_sdg(site)
result
#> == MineSDG Site SDG Scorecard ==
#> Site: FE-PILB (2024)
#> Composite score: 82.9 / 100 | Grade: B
#>
#> Goal-level scores (materiality-weighted):
#> Key: <sdg_goal>
#> sdg_goal mining_domain weight goal_score
#> <int> <char> <num> <num>
#> 1: 1 Economic Development 3 77.3
#> 2: 5 Community & Social Impact 3 65.2
#> 3: 6 Water & Resource Efficiency 5 69.0
#> 4: 7 Climate & Energy 4 98.0
#> 5: 8 Economic Development 4 93.0
#> 6: 12 Water & Resource Efficiency 4 93.0
#> 7: 13 Climate & Energy 5 100.0
#> 8: 15 Biodiversity & Land 5 65.6
#>
#> KPI detail available in $scorecard (14 KPIs).Drill into the KPI detail:
result$scorecard
#> kpi_id kpi_name sdg_goal
#> <char> <char> <num>
#> 1: ghg_intensity GHG Intensity (Scope 1+2) 13
#> 2: renewable_energy_pct Renewable Energy Share 7
#> 3: energy_intensity Energy Intensity 7
#> 4: water_recycling_rate Water Recycling Rate 6
#> 5: water_intensity Water Intensity 6
#> 6: land_rehabilitation_pct Land Rehabilitation Rate 15
#> 7: trifr Total Recordable Injury Frequency Rate 8
#> 8: ltifr Lost Time Injury Frequency Rate 8
#> 9: fatality_rate Fatality Frequency Rate 8
#> 10: female_employment_pct Female Employment Share 5
#> 11: local_employment_pct Local Employment Share 8
#> 12: community_investment_pct Community Investment Ratio 1
#> 13: tailings_ratio Tailings-to-Ore Ratio 12
#> 14: waste_rock_ratio Waste Rock (Strip) Ratio 12
#> value unit score
#> <num> <char> <num>
#> 1: 13.9500 tCO2e/kt ore 100.0
#> 2: 38.4000 % 96.0
#> 3: 0.1212 GJ/t ore 100.0
#> 4: 40.8800 % 38.0
#> 5: 0.3690 m3/t ore 100.0
#> 6: 55.9000 % 65.6
#> 7: 1.8320 per 1M hours 96.8
#> 8: 0.6410 per 1M hours 93.3
#> 9: 0.0000 per 1M hours 100.0
#> 10: 21.3000 % 65.2
#> 11: 69.0000 % 81.7
#> 12: 1.1710 % of revenue 77.3
#> 13: 0.3400 t/t ore 100.0
#> 14: 1.9760 t/t ore 86.1Score every site for the latest year:
latest <- demo_mine_sites[demo_mine_sites$year == 2024, ]
portfolio <- do.call(rbind, lapply(seq_len(nrow(latest)), function(i) {
s <- score_site_sdg(latest[i, ])
data.frame(site_id = s$site_id, composite = s$composite_score,
grade = s$grade)
}))
portfolio[order(-portfolio$composite), ]
#> site_id composite grade
#> 3 FE-PILB 82.9 B
#> 6 BX-ODIS 82.7 B
#> 4 CO-JHAR 70.5 B
#> 5 ZN-RAJA 68.5 C
#> 1 CU-ATAC 66.8 C
#> 2 AU-KALG 62.1 Cdemo_sdg_country mirrors the output of
fetch_sdg_country_data(), so the full analytics layer runs
without network access:
dt <- demo_sdg_country[demo_sdg_country$indicator == "6.4.1", ]
compute_sdg_stability(dt)
#> indicator country observations mean_value sd_value coefficient_of_variation
#> <char> <char> <int> <num> <num> <num>
#> 1: 6.4.1 AUS 9 38.42478 2.2049210 0.05738279
#> 2: 6.4.1 CHL 9 24.30078 1.5454462 0.06359657
#> 3: 6.4.1 IND 9 14.02433 1.4815603 0.10564212
#> 4: 6.4.1 ZAF 9 16.13933 0.8599564 0.05328327
#> volatility_index stability_category
#> <num> <char>
#> 1: 5.74 Moderately Stable
#> 2: 6.36 Moderately Stable
#> 3: 10.56 Moderately Stable
#> 4: 5.33 Moderately StableEverything above is wrapped in an interactive dashboard:
Five tabs: Portfolio Overview (composite scores and
grades per site), Site Deep-Dive (KPI trend lines and
the latest scorecard), SDG Alignment (ontology explorer
with materiality chart), KPI Registry, and
Data (bundled demo or a CSV upload following the
demo_mine_sites schema). Requires the shiny
package; DT is optional for enhanced tables.