softdataconsult/nigeria-net-zero-2060

Spatial-econometric assessment of Nigeria's net-zero-by-2060 pathway. Uses Moran's I, LISA cluster analysis, and spatial lag/error models on geocoded gas flaring data (2012-2024), plus a national ARDL model linking CO2 emissions, GDP, and energy use. Finds flaring clusters in Delta, Rivers, and Bayelsa states, driven by infrastructure density. # Pathways to Net-Zero by 2060: A Spatial-Econometric Assessment of Nigeria's Energy Transition Plan Implementation Research repository for a spatial-econometric assessment of Nigeria's net-zero-by-2060 pathway, combining sub-national gas flaring analysis with a national ARDL emissions-growth-energy model. **Author:** Isaac O. Ajao, Department of Statistics, Federal Polytechnic Ado-Ekiti, Nigeria ## Research Questions - **RQ1:** Is there significant spatial autocorrelation in onshore flaring intensity across Nigerian states? - **RQ2:** Do electricity access and onshore flare-site density predict state-level flaring intensity, and do they explain the RQ1 clustering? - **RQ3:** What is the long-run and short-run relationship between CO2 emissions, GDP, and energy use nationally, and is there evidence of a shift associated with the 2021 Climate Change Act? ## Repository Structure ``` scripts/ R scripts, run in numeric order (01-08) data/raw/ Source data (flaring, electricity, GDP, OWID CO2/energy) data/processed/ Cleaned/merged panels and model-ready datasets figures/ Final maps (choropleth, LISA cluster map) manuscript/ Research proposal and full manuscript draft (.docx) results/ Plain-text model output summaries (ARDL, spatial models) ``` ## Pipeline (run scripts/ in order) | Script | Purpose | |---|---| | `01_get_data.R` | Pulls national CO2/GDP/energy data (World Bank, OWID) | | `02_geocode_flares.R` | Geocodes site-level GGFR flare data to Nigerian states; splits onshore/offshore | | `04_spatial_model.R` | Moran's I (global), spatial lag/error models (RQ1, RQ2) | | `05_electricity_access.R` | Aggregates LGA-level NDHS electricity access to state level; merges with flaring | | `06_dhs_cluster_to_state.R` | Helper: DHS cluster-to-state crosswalk (if starting from raw DHS microdata) | | `07_ardl_model.R` | National ARDL bounds-testing model (RQ3) | | `08_maps.R` | Produces the two final figures (choropleth, LISA cluster map) | `03_get_n …