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Tutorial: Site-Level KPI Calculators

1. Why site-level KPIs?

Country-level SDG statistics tell you where a jurisdiction stands; they do not tell you how your operation performs. MineSDG v0.3.0 adds a family of site-level KPI calculators aligned with the disclosure conventions mining teams already report against:

KPI family	SDG	Framework convention
GHG intensity (Scope 1+2)	13	GHG Protocol; GRI 305-4; SASB EM-MM-110a.1
Energy intensity, renewable share	7	GRI 302; SASB EM-MM-130a.1
Water recycling, net consumption	6	GRI 303; ICMM Water Position Statement
Land rehabilitation rate	15	GRI 11.7 / 304-3; ICMM Principle 7
TRIFR / LTIFR / fatality rate	8.8	GRI 403-9; ICMM per-1M-hours convention
Workforce diversity & localisation	5, 8	GRI 405-1; GRI 202-2
Community investment ratio	1, 17	GRI 203-1; ICMM Principle 9
Tailings & waste-rock ratios	12	GRI 306 / 11.8; GISTM context

All calculators are pure functions — no network access, no side effects — so they can be embedded in pipelines, reports, and the Shiny dashboard.

2. Working with the demo data

The package bundles demo_mine_sites, a synthetic six-site, six-year panel:

head(demo_mine_sites[, 1:8])
#>   site_id      site_name commodity country year ore_processed_kt ghg_scope1_t
#> 1 CU-ATAC Atacama Copper    Copper     CHL 2019          18481.0       636010
#> 2 CU-ATAC Atacama Copper    Copper     CHL 2020          18020.1       653331
#> 3 CU-ATAC Atacama Copper    Copper     CHL 2021          19293.9       651551
#> 4 CU-ATAC Atacama Copper    Copper     CHL 2022          18580.4       564789
#> 5 CU-ATAC Atacama Copper    Copper     CHL 2023          18885.5       612173
#> 6 CU-ATAC Atacama Copper    Copper     CHL 2024          20762.1       640035
#>   ghg_scope2_t
#> 1       342467
#> 2       351794
#> 3       350835
#> 4       304117
#> 5       329632
#> 6       344634

Take one site-year:

site <- demo_mine_sites[demo_mine_sites$site_id == "CU-ATAC" &
                          demo_mine_sites$year == 2024, ]

3. Individual calculators

Climate (SDG 13):

calculate_ghg_intensity(
  scope1_t = site$ghg_scope1_t,
  scope2_t = site$ghg_scope2_t,
  ore_processed_kt = site$ore_processed_kt
)
#> $metric
#> [1] "SDG 13.2 - GHG Emissions Intensity"
#> 
#> $total_emissions_t
#> [1] 984669
#> 
#> $ghg_intensity
#> [1] 47.43
#> 
#> $scope1_share_percent
#> [1] 65

Energy (SDG 7):

calculate_energy_intensity(
  energy_gj = site$energy_gj,
  ore_processed_kt = site$ore_processed_kt,
  renewable_gj = site$energy_gj * site$renewable_energy_pct / 100
)
#> $metric
#> [1] "SDG 7.3 - Energy Intensity"
#> 
#> $energy_intensity
#> [1] 0.4722
#> 
#> $renewable_share_percent
#> [1] 40.1

Safety (SDG 8.8), per one million hours worked:

calculate_safety_performance(
  hours_worked = site$hours_worked,
  recordable_injuries = site$recordable_injuries,
  lost_time_injuries = site$lost_time_injuries,
  fatalities = site$fatalities
)
#> $metric
#> [1] "SDG 8.8 - Occupational Safety (per 1M hours, ICMM convention)"
#> 
#> $trifr
#> [1] 2.976
#> 
#> $ltifr
#> [1] 1.042
#> 
#> $fatality_rate
#> [1] 0
#> 
#> $hours_worked_millions
#> [1] 6.72

Water (SDG 6.4), land (SDG 15.3), community (SDG 1), waste (SDG 12):

calculate_water_efficiency(site$water_withdrawal_m3,
                           site$water_discharge_m3,
                           site$water_recycled_m3)
#> $metric
#> [1] "SDG 6.4 - Water Efficiency"
#> 
#> $net_consumption_m3
#> [1] 13631149
#> 
#> $recycling_rate_percent
#> [1] 40.83

calculate_land_restoration(site$land_disturbed_ha,
                           site$land_rehabilitated_ha)
#> $metric
#> [1] "SDG 15.3 - Land Restoration"
#> 
#> $percent_restored
#> [1] 56.52
#> 
#> $unrestored_area_ha
#> [1] 167.4

calculate_community_investment(site$community_investment_musd,
                               site$revenue_musd)
#> $metric
#> [1] "SDG 1.4 / 17.17 - Community Investment Ratio"
#> 
#> $community_investment_pct
#> [1] 1.014

calculate_waste_intensity(site$ore_processed_kt,
                          site$tailings_kt,
                          waste_rock_kt = site$waste_rock_kt)
#> $metric
#> [1] "SDG 12.4 / 12.5 - Mineral Waste Intensity"
#> 
#> $tailings_ratio
#> [1] 0.983
#> 
#> $waste_rock_ratio
#> [1] 2.678
#> 
#> $total_mineral_waste_kt
#> [1] 76011

4. Using your own data

Shape one row per site-year with the column names shown in ?demo_mine_sites. Any missing fields are simply skipped by the scorecard engine (next tutorial). A minimal example:

my_site <- data.frame(
  site_id = "MY-MINE", year = 2025,
  ore_processed_kt = 12000,
  ghg_scope1_t = 420000, ghg_scope2_t = 180000,
  hours_worked = 5.2e6, recordable_injuries = 18,
  lost_time_injuries = 6, fatalities = 0
)

calculate_ghg_intensity(my_site$ghg_scope1_t, my_site$ghg_scope2_t,
                        my_site$ore_processed_kt)$ghg_intensity
#> [1] 50

Continue with vignette("sdg-ontology-and-scorecard") to turn these raw KPIs into a weighted 0-100 SDG scorecard, or launch the dashboard with run_minesdg_dashboard().

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.