Increasing GHG emissions is one of the primary causes of climate change. Globally, GHGs have increased drastically since the industrial revolution and are expected to continue rising [1]. Considerable evidence shows that the highest portion of climate change is mainly caused by the emission of GHGs due to anthropogenic activities, particularly carbon dioxide (CO₂) from fossil fuel combustion, and could accelerate the temperature increase in the future [1,2,3]. South Africa was shown to be one of the world’s largest per capita GHG emitters, due to its energy-intensive industries and the high share of coal use [4]. This is mainly due to high dependence on coal, which accounted for 91% of its electricity generation in 2016 [5]. A fossil fuels-intensive energy, transport, and economy have shown to be the biggest culprits in South Africa. However, in recent years, South Africa has taken steps towards clean energy generation. The African Development Bank estimates that renewable energy generation (mainly through wind and solar) will continue to grow in the future [4]. In order to transform to low-carbon energy and economy, under the Copenhagen Accord, South Africa committed to cut emissions by 34% from business as usual (BAU) by 2020, and by 42% by 2025. South Africa released the “Intended Nationally Determined Contribution” (INDC) that builds on mitigation targets by moving from a relative deviation from BAU to an absolute PPD (“peak, plateau, decline”) trajectory, where GHG emissions should peak by 2020, plateau until 2030, and begin to decline after 2030. The INDC has been updated and the expectation is that emissions should plateau from now until 2030. The National Climate Change Response White Paper (NCCRWP) of 2011 presents the country’s vision for an effective climate change response and the long-term transition to a climate-resilient low-carbon economy and society [6]. South Africa identifies several policy mechanisms to achieve reductions including sectoral emission reduction targets and company-level carbon budgets. Therefore, steps should be taken to reduce these GHG emissions and help the country to achieve its international commitments but more so to achieve clean air human rights in the country. The Carbon Tax Act [7] was signed into law to enforce policy measures that will support international commitments such as the 2015 Paris Agreement and ensure a cost-effective transition to a low-carbon economy in all the sectors in the country. South Africa has recently developed a Low Emission Development Strategy (LEDS) which it plans to submit as a commitment towards achieving its Paris commitments and goals [8]. The Carbon Tax Act together with the Greenhouse Gas Emissions reporting, Climate Change Bill, and Pollution Prevention Plan regulations are seen as substantial policy steps undertaken by the country to curb GHG emissions [9]. According to South Africa’s Low Emission Development Strategy 2050, the energy sector contributed 79.5% of the total gross emissions for South Africa in 2015 and grew by 25% between 2000 and 2015. South Africa, in partnership with its climate change stakeholders and role players (including NBCMs), continues to strengthen their efforts of achieving and stabilizing GHG concentrations in the atmosphere, hence reducing its carbon footprints. The valuable first step towards emission reduction by stakeholders is quantifying the GHG emissions due to various human and organizational activities [10].

The NBCM is important nationally and globally for tourism and more importantly Agriculture, Forestry, and Other Land Use (AFOLU) sustainability. The different activities under tourism management (energy use at accommodations and transport in resident versus destination country) have also been shown to contribute 8% to global GHG emissions [11]. Although South Africa’s commitment is considered inadequate according to the Carbon Brief, unlike in other countries wherein the belief systems of leaders have been shown to be important barriers to progress on the decarbonization of tourism [12], SANParks leadership supports the global low-carbon transition. The AFOLU sector contributes to 20–24% of global emissions although most of it is from crop and livestock agriculture [13]. The total net emissions for South Africa between 2000 and 2015 increased by 20.2% when including AFOLU but it increased to 23.1% when excluded [8,9]. Under AFOLU, conservation management contributes in two ways: either as a carbon emission source or sink with a lot of focus on its value for C storage rather than a source. According to the AFOLU report of 2016 [14], it is recommended that in order to understand the GHG emissions and the stocks of a country, the benchmark National GHG Inventory and National Terrestrial Carbon Sink Assessment provided were important as the first baseline emissions for the AFOLU sector in South Africa.

Organizations in the sector must take a prominent role in helping countries to achieve their commitments towards reducing GHGs. It is thus important for NBCMs to initiate baseline GHGs inventories if they are non-existent. It is also necessary to understand the contribution of NBCM at an organizational and individual level to the sector and thus the country. As a state entity, SANParks will be required to develop such a baseline on GHG inventory and conduct a carbon footprint assessment. A leader in NBCM must demonstrate how it would reduce emissions at these multiple levels.

Carbon footprint assessment has been used in different contexts, for example in the agriculture sector [15], in the pulp and paper industry [16], in wastewater collection, transport, and treatment [17], and so forth (energy, tourism, and on a limited basis in the NBCM). Essentially, there are two approaches or models, namely, the process-based and the input-output model. The process-based model uses a bottom-up approach that involves a detailed life-cycle of the product while the input-output approach uses a top-down approach wherein carbon intensities are assigned at a product category level [11,18,19]. The process-based life cycle assessments or carbon footprints are generally more representatives of the modeled product system, but they are not necessarily more accurate. The process-based assessments are well known for their truncation error and are subject to errors of up to 50% of total impacts. However, the development of hybrid models that combine input-output and process-based approaches would be better [17,20]. The US National Park Service (NPS) has developed its own carbon footprint assessment tool for achieving its Green Parks Plan at a park level and therefore organization level that is inclusive of tourists and their concessionaires.

2. SANParks Emission

Scope 1 included all emissions from sources owned or controlled by the reporting company. Scope 2 included emissions associated with the consumption of purchased electricity that is not owned or controlled by the reporting company. Scope 3 included all indirect emissions that are a consequence of activities of a company but occur at sources owned or controlled by another company(Table 1).

Table 1. Activity data and data providers for each emission source reported on.

Scope	Emission Sources	Activity Data	Data Collection and Status Evaluation	Formula Applied
Scope 1: Direct emissions	SANParks vehicles	Km and vehicle details	Vehicles were grouped in three groups according to car model (≤1.7 L, > 2-L petrol, and ≤1.7 liters, >2 L diesel) and distance (km) traveled were calculated as per the model of the vehicles. The distance was calculated from each vehicle’s odometer.
	Fuel	Liters and fuel type	Data recorded from receipt. However, a formula was derived from the estimated total fuel liters used for BNP, GNP, MNPWHS, Marakele, and TMNP.	Liters = Amount (Rand)/ Price per liter
Scope 2: Indirect emissions	Electricity	KwH used	Data captured from electricity bills. For the few months where data were not provided, the study used the average of that specific financial year.
Scope 3: Indirect emissions	Air travel	Routing details	The distance was calculated from the routing details of all flights (both international and local).
	Car rental	Km and vehicle details	Vehicles were grouped into four groups according to the descriptions and details of the specific vehicle (≤1.7 and >2 L petrol and diesel).
	Staff vehicle	Km and vehicle details	Vehicles were grouped as <1.7, ≤1.7 L and > 2-L petrol vehicles and <1.7, ≤1.7 L, and > 2-L diesel vehicles.
	Solid waste	Tons	Some formulas were derived to calculate waste per park every month in relation to the total number of guests.	Y = mx + c Therefore: x = (y − c)/m
	Water	Kiloliters	Derived formula to calculate water consumption for parks that depend on groundwater supply where 17 is the number of liters that water pump can push per minute, T is time in minutes that water pump run per day, and t is the total number of tanks	Water usage = (17) × (T) × (t)

Over the reference period, SANParks emitted an average of 73,732 tCO₂e per year. This does not include emissions reduction through the installation of solar panels presented in Table 4. It also does not include the carbon offset generated by having a large carbon sink which the paper did not cover as the focus was on GHG emissions rather than C storage. Global emissions increased by 0.6% from 2018–2019 [31]. Over the 2015–2019 period, SANParks’ emissions increased by 0.02% on average. As expected, the rate of increase for SANParks was less than the global and national rate of increase. In order to understand SANParks’ contribution to the national and the SA’s AFOLU sector, recent studies used the year 2015 value of 68,812 tCO₂e compared with the national figure which amounted to 531 million tCO₂e and for the AFOLU sector was 49.5 million tCO₂e. SANParks’ contribution equates to 0.01% of total national emissions and 0.14% for the AFOLU sector. The results illustrate that electricity usage contributed a total of 40,681 t of CO₂e, followed by fuel combustion which contributed 26,088 t of CO₂e, and solid waste contributing 2937 tCO₂e to the total SANParks carbon footprint.

On an annual basis, total emissions ranged between 68,487 tCO₂e in 2014/2015 and 75,903 tCO₂e in 2017/2018, the latter was higher than 2018/2019. The highest rate of increase in emissions occurred between 2014/2015 and 2015/2016 by 7%, while the smallest increase occurred between 2016/2017 and 2017/2018 by 0.9%. A decline of 0.4% occurred between 2017/2018 and 2018/2019. The average increase rate over the five-year period was 2.5%, however from 2016/2017 after SANParks committed to reducing energy emissions, there was an overall decline of 1.4% annually.

3. Contribution of Scope Emissions to Total SANParks and Individual Park Emissions

A large proportion of GHG emissions was from Scope 2 and 1, in that order. Scope 2 was mainly electricity (40,681 tCO₂e), and it accounted for 55% of the total SANParks carbon footprint. For Scope 1, fuel usage accounted for 35.4% of the total scope emissions (27,391 tCO₂e), while SANParks vehicles accounted for 1.8%, thereby accounting for 37.2% of the total SANParks carbon footprint. Altogether, these two largest contributing scopes accounted for about 92% of the total SANParks emissions. For the organization to achieve the greatest reduction impact, these should be the scopes that the organization targets the most. Scope 3 accounted for the remaining 8% of the total SANParks emissions, of which waste accounted for 4% followed by staff vehicles at 1.6% and the remainder was made up of emissions resulting from water, flights, and rental cars.

Scope 1 emissions within each park contributed on average 15% to the total individual park emissions. However, the highest contribution from Scope 1 to specific park emissions occurred in Tankwa Karoo National Park (TKNP) (80%) followed by KNP (48%) and Kalahari Gemsbok National Park (KGNP) (43%). The smallest contributions came from Groenkloof National Park (GNP) (1.1%), Augrabies Falls National Park (AFNP) (2.6%), West Coast National Park (WCNP) (2.7%), Golden Gate Highland National Park (GGHNP) (3.3%), Mokala National Park (MNP) (3.4%), Namaqua National Park (NNP) (4.3%), and TMNP (5.2%).

On average across all national parks and offices, Scope 2 contributed 66% to the per park total emissions. However, the highest contribution from Scope 2 to a specific park’s emissions occurred in GGHNP (91.3%) followed by MNP (88.9%), AFNP (88.4%), Karoo National Park (KRNP) (87.3%), Mountain Zebra National Park (MZNP) (87.2%), Marakele National Park (MRNP) (83.1%), WCNP (81.9%), and GRNP (81.2%). The smallest contributions came from TKNP (0%) and NNP (33.8%).

In the 20 national parks and Kimberly office, Scope 3 contributed 18.7% on average to the per park total emissions. However, the highest contribution from Scope 3 to the specific park emissions occurred in NNP (48.9%) followed by GNP (41.8%), Bontebok National Park (BNP) (41.3%), and Camdeboo National Park (CNP) (35.8%). The smallest contributions came from KNP (4.8%), GGHNP (5.4%), KGNP (6.6%), MNP (7.7%), MZNP (8.5%), and KRNP (8.6%)

4. Emissions Per Park

The most contributing park to SANParks’ total emissions was KNP at 53,169 tCO₂e. This equates to a total contribution of 72% to the total SANParks’ emissions. The relative frequency of individual park emissions suggested that 16 national parks, including Kimberly office, contributed less than 2000 tCO₂e while 4 national parks accounted for above 2000 tCO₂e. The latter are responsible for the most emissions recorded for SANParks, responsible for 84% of SANParks emissions.

5. Contribution of Park Scope Emissions to Total SANParks Scope Emissions

Under Scope 1, the highest contributing individual park towards the total SANParks Scope 1 emissions was KNP (25,302 tCO₂e), which accounted for 92% of the total SANParks scope 1 emissions. The higher emissions from fuel usage for stationary combustion within the Scope 1 came from KNP, GRNP, AENP, KGNP, TKNP, and Richtersveld National Park (RNP); these parks accounted for 99% of total emissions from fuel combustion. For the remaining source under Scope 1, KNP had the highest emissions from SANParks-owned vehicles too, 610 tCO₂e, followed by GRNP (152 tCO₂e) and TMNP (117 tCO₂e). Table Mountain National Park had the most emissions coming from SANParks vehicles compared to fuel usage under this Scope. The moderate contribution was from both RNP and GGHNP. The least contributing parks to total Scope 1 emissions of SANParks were BNP, WCNP, GNP, Agulhas National Park (ANP), MNP, and NNP, accounting for 10, 12, 17, 20, 21, and 22 tCO₂e, respectively.

In Scope 2, KNP was the top contributing park accounting for 25,301 tCO₂e followed by GRNP at 3308 tCO₂e, TMNP at 2000 tCO₂e, GGHNP (1766 tCO₂e), and AENP (1697 tCO₂e). This equates to a contribution of 62%, 8%, 5%, 4%, and 4%, respectively. Together, these parks were responsible for 34,072 tCO₂e, which translated to 83% of the total SANParks Scope 2 emissions (40,681 tCO₂e). The moderately contributing parks were AFNP (1005 tCO₂e), KGNP (915 tCO₂e), GNP (861 tCO₂e) and KRNP (767 tCO₂e). The least contributing parks were TKNP, NNP, and Camdeboo National Park (CNP), responsible for 0, 43, and 79 tCO₂e, respectively.

In Scope 3, KNP was the top contributing park towards the total Scope 3 emissions for SANParks and accounted for 2566 tCO₂e followed by TMNP at 632 tCO₂e, GNP at 631 tCO₂e, and GRNP (461 tCO₂e). This equates to a contribution of 45.3%, 11.2%, 11.1%, and 8.1%, respectively to the total SANParks Scope 3 emissions. Together, these parks were responsible for 4290 of 5661 tCO₂e, which translated to 75.8% of the total SANParks Scope 3 emissions. The moderately contributing parks were AENP (224 tCO₂e), KGNP (122 tCO₂e), GGHNP (104 tCO₂e), Kimberly office (103 tCO₂e), Addo Elephant National Park (AFNP) (103 tCO₂e), and Mapungubwe National Park and World Heritage Site (MNPWHS) (94 tCO₂e). The least contributing parks were WCNP, TKNP, ANP, MNP, and MZNP, responsible for 0.6, 0.7, 0.7, 0.8, and 1 tCO₂e, respectively.

Groenkloof National Parks had high emissions intensity followed by KNP, TMNP, and GRNP, representing 237, 0.99, 0.31, and 0.24 tCO₂e/ha, respectively. The least contributing parks in terms of emissions intensity were NNP, RNP, and TKNP. Groenkloof National Park had the lowest per capita, followed by NNP, MNPWHS, and CNP, which accounted for 1.4, 4.3, 4.5, and 4.7 tCO₂e/ employee, respectively. The highest emissions per capita came from AFNP, KNP, and KRNP, accounting for 24, 21, and 16 tCO₂e/employee, respectively.

6. Relationships between Total SANparks’emissions, Park Size, Built Size, Labor, Scope 1, Scope 2, and Scope 3

The results showed no significant relationship between total SANParks’ emissions and total park sizes; however, there was a significant relationship between total SANParks’ emissions and park size, building size, labor, Scope 1, Scope 2, and Scope 3. Similarly, there was no significant relationship between park size and building size, number of employees, Scope 2, and Scope 3, yet, there was a significant relationship between park size and Scope 1. The analysis showed a relationship between building size and the number of employees, Scope 2, and Scope 3, however, there was no significant relationship between building size and Scope 1. There was no significant relationship between the number of employees and Scope 1, however, the results showed a significant relationship between the number of employees and Scope 2 as well as Scope 3. There was no significant relationship between Scope 1 and the other Scopes. Finally, the results showed a significant relationship between Scope 2 and Scope 3.

7. SANParks Electricity Emissions under Current and Future Scenario

The results for electricity emissions reduction under current and future scenarios show that all scenarios remain almost similar in 2025 except for the Dolphin and especially under the Peregrine Falcon scenario. For example, the total emissions for SANParks will move from the current value of 40,681 tCO₂e to 43,044 tCO₂e under the Lion scenario, while for the Orb-web Spider scenario it will decline to 38,429 tCO₂e, whereas the Leopard Toad scenario will decline to 35,316 tCO₂e. The greatest decline is from the Peregrine Falcon scenario at 506 tCO₂e followed by the Dolphin scenario at 22,350 tCO₂e. The former shows the most significant decline in emissions by 2025 and neutrality is almost achieved by 2030 with emissions equaling only 39 tCO₂e.

The Lion scenario shows a steady but sharp increase by 2050 under the current trajectory of emissions from 43,044 to 44,816 tCO₂e by 2030 and ultimately reaching 52,663 tCO₂e in 2050. The Orb-web Spider shows a slightly bigger margin of decline at 36,898 tCO₂e compared to the Lion scenario at 44,816 tCO₂e in 2030; however, it was slightly higher in emission when compared with the Leopard Toad scenario with its value of 31,923 tCO₂e. The Dolphin scenario cut emissions by more than half from 2025 levels to 14,571 tCO₂e in 2030 and therefore appears to be way better than former scenarios in 2030.

SANParks’ commitment of 2% year-on-year does not achieve neutrality under the Leopard Toad scenario by 2050 despite reducing emissions from 35,316 in 2025 to 21,312 tCO₂e, while the Dolphin remains the most efficient reducing emissions from 14,571 in 2030 to 2632 tCO₂e by 2050. The Peregrine Falcon scenario achieves neutrality as early as in the vicinity of 2030. This work evaluated cost-saving under the different scenarios below to check the value for money of each scenario.