ESG Reporting: GRI, ICMM and BRSR
MineSDG v0.4.0 turns the site KPI engine into a reporting engine.
Instead of chaining calculate_*() primitives, one call
produces a framework report:
rep <- generate_gri_report(demo_mine_sites, site_id = "CU-ATAC",
years = 2022:2024)
rep
#> == MineSDG GRI Report ==
#> Site: CU-ATAC | Reporting year: 2024
#> Composite: 66.8 / 100 | Grade: C
#> Disclosures: 25 | reported: 19 | narrative pending: 5 | mandatory coverage: 77.3%
#> Render with render_minesdg_report(); export tables with write_report_xlsx().
#> Version: 0.4.0 | crosswalk: gri:2021+g11:2022/map-v1
The disclosure bundle
Every report is built from a disclosure bundle – the
only interface between the scoring engine and the reporting layer:
Mine KPI Data -> KPI Engine -> Score Engine -> Disclosure Bundle
-> Framework Mapping -> Report Generator
compile_site_disclosures() runs
score_site_sdg() once per year and assembles KPI values,
scores, validated raw inputs, year-on-year deltas and your narrative
text. Report generators never call KPI calculators directly, which
guarantees a single source of truth and zero duplicate calculations.
bundle <- compile_site_disclosures(
demo_mine_sites, site_id = "CU-ATAC", years = 2022:2024,
narratives = list(
nar_water_mgmt = "Site water is managed under a catchment-level
stewardship plan with quarterly community review."),
entity_meta = list(company = "Atacama Copper SpA"))
bundle
#> == MineSDG Disclosure Bundle ==
#> Site: CU-ATAC - Atacama Copper
#> Years: 2022, 2023, 2024 | Reporting year: 2024
#> Composite (2024): 66.8 / 100 | Grade: C
#> KPIs per year: 14 | Raw fields: 21
#> Narrative slots supplied: 1
bundle_kpi(bundle, "ghg_intensity")
#> [1] 47.43
bundle_raw(bundle, "ghg_scope1_t")
#> [1] 640035
bundle_score(bundle, "goal_8")
#> [1] 86.3
Framework mappings are data
Each framework ships as a crosswalk dataset with a shared schema.
Adding or amending a disclosure means editing data
(data-raw/make_crosswalks.R), not code:
framework_crosswalk("gri")[1:6, c("disclosure_id", "disclosure_title",
"source_type", "source_id")]
#> disclosure_id disclosure_title source_type
#> <char> <char> <char>
#> 1: 302-1 Energy consumption within the organization raw
#> 2: 302-1b Renewable share of energy consumption raw
#> 3: 302-3 Energy intensity kpi
#> 4: 303-1 Interactions with water as a shared resource narrative
#> 5: 303-3 Water withdrawal raw
#> 6: 303-4 Water discharge raw
#> source_id
#> <char>
#> 1: energy_gj
#> 2: renewable_energy_pct
#> 3: energy_intensity
#> 4: nar_water_mgmt
#> 5: water_withdrawal_m3
#> 6: water_discharge_m3
The generic mapper resolves every disclosure to a value and an honest
status – reported, partial,
narrative_provided, narrative_required or
not_in_scope. Reports never fabricate:
m <- map_bundle_to_framework(bundle, "gri")
m
#> == MineSDG Framework Mapping: GRI ==
#> Site: CU-ATAC | Reporting year: 2024
#> Disclosures: 25
#> reported: 19 | partial: 0 | narrative provided: 1 | narrative required: 4 | not in scope: 1
#> Mandatory coverage: 18 / 22 (81.8%)
GRI
generate_gri_report() covers GRI 302, 303, 304, 305,
306, 403 and 413, and produces the content index assurance teams ask
for:
head(gri_content_index(rep), 8)
#> disclosure_id disclosure_title status
#> 1 302-1 Energy consumption within the organization reported
#> 2 302-1b Renewable share of energy consumption reported
#> 3 302-3 Energy intensity reported
#> 4 303-1 Interactions with water as a shared resource narrative_required
#> 5 303-3 Water withdrawal reported
#> 6 303-4 Water discharge reported
#> 7 303-5 Water consumption reported
#> 8 303-R Water recycling rate reported
#> source omission_reason
#> 1 raw:energy_gj
#> 2 raw:renewable_energy_pct
#> 3 kpi:energy_intensity
#> 4 narrative:nar_water_mgmt Narrative disclosure pending
#> 5 raw:water_withdrawal_m3
#> 6 raw:water_discharge_m3
#> 7 kpi:water_intensity
#> 8 kpi:water_recycling_rate
ICMM
generate_icmm_report() adds a board scorecard, a
traffic-light assessment (a pure reclassification of existing 0-100 KPI
scores) and data-driven recommendations from
icmm_recommendation_rules:
icmm <- generate_icmm_report(demo_mine_sites, site_id = "CU-ATAC",
years = 2023:2024)
icmm$extras$traffic_lights[, c("kpi_id", "value", "score", "light")]
#> kpi_id value score light
#> <char> <num> <num> <char>
#> 1: tailings_ratio 0.9830 4.3 red
#> 2: water_recycling_rate 40.8300 37.9 red
#> 3: female_employment_pct 21.1000 64.4 amber
#> 4: land_rehabilitation_pct 56.5200 66.5 amber
#> 5: community_investment_pct 1.0140 66.5 amber
#> 6: ghg_intensity 47.4300 69.1 amber
#> 7: energy_intensity 0.4722 69.3 amber
#> 8: waste_rock_ratio 2.6780 76.0 green
#> 9: local_employment_pct 66.2000 77.0 green
#> 10: water_intensity 0.8760 77.3 green
#> 11: ltifr 1.0420 82.2 green
#> 12: trifr 2.9760 85.9 green
#> 13: renewable_energy_pct 40.1000 100.0 green
#> 14: fatality_rate 0.0000 100.0 green
icmm$extras$recommendations[, c("principle", "recommendation")]
#> principle
#> <char>
#> 1: Principle 6
#> 2: Principle 6
#> recommendation
#> <char>
#> 1: Invest in water recirculation: thickened-tailings water recovery and process-water reuse to raise recycling rates.
#> 2: Review tailings minimisation options (ore sorting, coarse particle recovery) and confirm GISTM conformance.
BRSR
The SEBI BRSR generators reuse the same bundle. Supply
entity_meta$fx_usd_inr to convert monetary lines to INR
lakh – without it, those lines are flagged
partial/not_in_scope rather than
estimated:
kpis <- generate_brsr_kpis(
demo_mine_sites, site_id = "CO-JHAR", years = 2023:2024,
entity_meta = list(fx_usd_inr = 83.2))
kpis[, c("disclosure_id", "disclosure_title", "value", "status")]
#> disclosure_id disclosure_title value status
#> <char> <char> <num> <char>
#> 1: BRSR-C-P2-E1 Resource efficiency in production 3.265000e-01 reported
#> 2: BRSR-C-P3-E1 Safety incidents (TRIFR) 5.655000e+00 reported
#> 3: BRSR-C-P3-E2 Lost-time injuries (LTIFR) 1.562000e+00 reported
#> 4: BRSR-C-P3-E3 Fatalities 0.000000e+00 reported
#> 5: BRSR-C-P3-L1 Training intensity NA not_in_scope
#> 6: BRSR-C-P5-E1 Workforce diversity 1.960000e+01 reported
#> 7: BRSR-C-P6-E1 Energy consumption 9.339240e+06 reported
#> 8: BRSR-C-P6-E1b Renewable energy share 3.310000e+01 reported
#> 9: BRSR-C-P6-E2 Water withdrawal 1.375359e+07 reported
#> 10: BRSR-C-P6-E2b Water recycling 3.709000e+01 reported
#> 11: BRSR-C-P6-E3 GHG emissions (Scope 1) 1.394212e+06 reported
#> 12: BRSR-C-P6-E3b GHG emissions (Scope 2) 7.507290e+05 reported
#> 13: BRSR-C-P6-E3c GHG intensity 7.499000e+01 reported
#> 14: BRSR-C-P6-E4 Waste generated 1.003739e+05 reported
#> 15: BRSR-C-P6-L1 Land rehabilitation 6.108000e+01 reported
#> 16: BRSR-C-P8-E1 CSR / community spend 1.414000e+00 reported
#> 17: BRSR-C-P8-E2 Local employment 6.530000e+01 reported
#> 18: BRSR-C-P8-L1 Local procurement NA not_in_scope
generate_brsr_report(),
generate_brsr_sectionA() and
generate_brsr_sectionB() are views over the same single
mapping computation.
Rendering
Reports are plain R objects; rendering is optional and gated on
Suggests packages:
# HTML / PDF / DOCX via the packaged Quarto templates
render_minesdg_report(rep, "gri_2024.html", format = "html")
render_minesdg_report(rep, "gri_2024.pdf", format = "pdf")
# Styled Excel workbook of the disclosure tables
write_report_xlsx(rep, "gri_2024.xlsx")
# One-liner: generate and render together
generate_brsr_report(demo_mine_sites, site_id = "CO-JHAR",
years = 2023:2024, output = "brsr.docx",
format = "docx")
Portfolio and radar views
score_portfolio_sdg(demo_mine_sites)[year == 2024]
#> site_id site_name commodity country year composite_score grade
#> <char> <char> <char> <char> <int> <num> <char>
#> 1: FE-PILB Pilbara Iron Iron Ore AUS 2024 82.9 B
#> 2: BX-ODIS Odisha Bauxite Bauxite IND 2024 82.7 B
#> 3: CO-JHAR Jharia Coal Coal IND 2024 70.5 B
#> 4: ZN-RAJA Rajasthan Zinc Zinc IND 2024 68.5 C
#> 5: CU-ATAC Atacama Copper Copper CHL 2024 66.8 C
#> 6: AU-KALG Kalgoorlie Gold Gold AUS 2024 62.1 C
plot_sdg_radar(bundle)
plot_domain_radar(bundle)
The dashboard (run_minesdg_dashboard()) exposes the same
functions interactively, including new Benchmark and Radar tabs.