Mpumalanga Province is situated in the northeast of South Africa, sharing its north and south east borders with Mozambique and Swaziland, and its national borders with Limpopo, Gauteng, Free State and KwaZulu-Natal. Most of the provincial land is on the high plateau grasslands of the Middleveld, with mountainous regions in the north-eastern parts of the province. Mpumalanga is the second-smallest province after Gauteng, covering an area of 76,495 square kilometers (6.3% of of South Africa’s land area). The province has a population of 4,229,300 people making up 7.8% of South Africa’s total population. Mpumalanga comprises of 21 municipalities: three districts (Ehlanzeni, Nkangala and Gert Sibande) further divided into 18 local municipalities. It’s capital city is Mbombela, previously known as Nelspruit, located in the Mbombela Local Municipality and is the administrative and business hub of the Lowveld (MCCVA, 2015; MPSoER, 2008; Odunyi et al., 2013). Mpumalanga is rich in coal reserves and therefore has substantial coal mining activity and an active mining sector. It produces close to 90% of South Africa’s coal and is home to three of the biggest coal power stations in Southern Africa. Other important mineral resources mined in the area include chrome, steel, vanadium, asbestos, magnesite, iron ore, vanadium, limestone, dolomite, silica and construction materials some of which are an important source of exports for South Africa (PGDS, 2004). In addition to mining, other major sectors in the province include forestry and agriculture. The province has a large fruit and vegetable market which accounts for a significant percentage contribution towards the province’s economy (PGDS, 2004).
Greenhouse Gas Data
There are a range of sources of Greenhouse Gas (GHG) emissions at the provincial and district level. One of the main sources of GHGs is from electricity. Although the GHGs associated with the generation of electricity is recorded at a national level, provinces and municipalities typically record the GHGs associated with the sale of electricity. The map below is a summary of the GHGs from the sale of electricity in the province. This data is sourced from Stats SA for the provinces and divided into district data by the proportion of houshold numbers in the district.
Liquid Fuel Sales
A second major source of GHGs is from the sale of liquid fuels. This includes jet fuel, aviation gasoline, diesel, furnace oil, LPG, paraffin and petrol. The map below is a summary of the GHGs from the sale of liquid fuels in the province. Each fuel is converted to Gigagram Carbon Dioxide equivalent (GgCO2e) using specific emission factors from the Intergovernmental Panel on Climate Change Emission Factor Database.
Key Climate Hazards
The figure below shows projected changes in annual average temperatures, highlighting increasing temperatures throughout the province for the period 2021-2050 under the RCP 8.5 scenario. By 2050, the province is projected to be affected by higher annual average temperatures, which will adversely affect water and food security. Evaporation rates will also likely increase and agricultural outputs may reduce.
Increasing rainfall variability
The figure below shows projected shifts in annual average rainfall throughout the province between 2021-2050 under the RCP 8.5 scenario. Annual average rainfall amounts vary across the province. There is uncertainty regarding projected future rainfall.
Increasing storms and flooding events
The figure below shows projected changes in the annual average number of extreme rainfall days throughout the province over the period 2021-2050 under the RCP 8.5 scenario. Increases in the number of rainfall days are likely to result in an increase in intense storms, and flooding events across the province.
The current delineation of biomes is depicted in the figure below, with the predicted shift in biomes shown in the following figure based on a high-risk scenario. The biomes have varying sensitivities to the projected impacts of climate change which are further exacerbated by issues such as the fragmentation of natural areas and unsustainable water usage rates.
Climate Change Vulnerability
The CSIR Greenbook has developed and refined a vulnerability assessment framework by collating relevant data into composite vulnerability indicators. Four local municipality level vulnerability indices were computed and are shown spatially below.
Social inequalities are the factors that affect the susceptibility and coping mechanisms of communities and households. Indicators for social vulnerability attempt to consider the sensitivity, response and recovery from the impacts of natural hazards. The CSIR Green Book has developed a socio-economic vulnerability index that is measured on a scale from 1 (low vulnerability) to 10 (high vulnerability). The map below shows the Socio-Economic vulnerability score of each municipality in the province visually.
Environmental vulnerability describes the vulnerability and risk to the natural environment and the impacts on the ecological infrastructure of which surrounding settlements are dependent. The environmental risk of an area includes ecosystems, habitats, physical and biological processes (reproduction, diversity, energy ﬂows, etc). The CSIR Green Book has developed an Environmental Vulnerability Index that is measured on a scale from 1 (low vulnerability) to 10 (high vulnerability). The map below shows the environmental vulnerability score of each municipality in the province visually.
Physical vulnerability describes the physical fabric and connectedness of settlements (buildings and infrastructure) and focuses mainly on the conditions that exist before a hazard occurs and the expected level of resulting loss. The CSIR Green Book has developed a physical vulnerability index that is measured on a scale from 1 (low vulnerability) to 10 (high vulnerability). The map below shows the physical vulnerability score of each municipality in the province visually.
Economic vulnerability describes the potential risks posed by hazards on economic assets and processes. Potential hazards can include job losses, increased poverty and interruptions in business activities. The CSIR Green Book has developed an economic vulnerability index that is measured on a scale from 1 (low vulnerability) to 10 (high vulnerability). The map below shows the economic vulnerability score of each municipality in the province visually.
- CSIR. 2019. ‘Green Book | Adapting South African Settlements to Climate Change’. Green Book | Adapting South African Settlements to Climate Change. 2019. www.greenbook.co.za.
- Le Roux, A, E van Huyssteen, K Arnold, and C Ludick. 2019. ‘The Vulnerabilities of South Africa’s Settlements’. Green Book. 2019. https://pta-gis-2-web1.csir.co.za/portal/apps/GBCascade/index.html?appid=280ff54e54c145a5a765f736ac5e68f8.
- SANParks. 2011a. ‘CCAB – Current Biome Delineations 2011 [Vector Geospatial Dataset]’. Available from the Biodiversity GIS website. http://bgis.sanbi.org/SpatialDataset/Detail/484
- SANParks. 2011b. ‘CCAB – High Risk Scenarios – Biome Delineations 2011 [Vector Geospatial Dataset]’. Available from the Biodiversity GIS website. http://bgis.sanbi.org/SpatialDataset/Detail/486.
Stats SA 2020: Electricity Sales (ES) from Stats SA by Province Source: http://www.statssa.gov.za/?
page_id=1854&PPN=P4141&SCH= 72843IPCC 2020. Intergovernmental Panel on Climate Change Emission Factor Database https://www.ipcc-nggip.iges. or.jp/EFDB/main.php