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versão On-line ISSN 2071-0771
versão impressa ISSN 0075-6458

Koedoe vol.60 no.1 Pretoria  2018 



The operational competitiveness of public protected areas managed by Ezemvelo KZN Wildlife



Karabo D. Motau; Edilegnaw Wale

Discipline of Agricultural Economics, University of KwaZulu-Natal, South Africa





The purpose of this study was to measure the operational competitiveness of public protected areas (PPAs) in the KwaZulu-Natal province, South Africa. Financial data for ecotourism operations in PPAs were collected from Ezemvelo KZN Wildlife (EKZNW) for 2007-2013, to construct an operational competitiveness profile for each PPA by using a non-parametric method called operational competitiveness rating analysis (OCRA). The results show that permanent staff, utilities, maintenance and repairs, and cost of sales were cost items with the highest average share of total costs, whereas accommodation, admissions, sales and tours, and rides and hikes received higher average shares of total revenues for most PPAs. The identification of the most important cost and revenue items was followed by the computation of resource consumption and revenue generation inefficiency ratings from 2007 to 2013, with the results showing that resource competitiveness had more impact on operational competitiveness relative to revenue competitiveness. This suggests that PPAs under EKZNW can improve operational competitiveness by reducing costs. Ecotourism is an economic incentive used in several countries to encourage biodiversity conservation. Because of declining public funding, conservation agencies such as EKZNW in South Africa should find new sources of funding or find cost-effective ways of managing ecotourism operations.
CONSERVATION IMPLICATIONS: This information will provide insights into the quality of operational efficiencies of ecotourism activities at EKZNW-controlled PPAs and motivate management to adopt cost-cutting and revenue-increasing strategies to improve operational competitiveness.




The ownership of wildlife largely remains under state control in several countries, managed by conservation agencies (Muir-Leresche & Nelson 2000). However, despite government protection of wildlife and biodiversity, a record number of species are classified as threatened, endangered or vulnerable by the International Union for Conservation of Nature (IUCN), because of pressures emanating from economic activities such as agriculture, mining, timber production and poaching (Damania & Hatch 2005). Conservationists recognise that to prevent further deterioration, the preservation of many species rests on establishing their economic value and providing incentives for sustainable use (Baker 1997; Lindsey, Romanach & Davies-Mostert 2009).

Ecotourism as an economic incentive has become a tool for biodiversity conservation in public protected areas (PPAs) of many developing countries (Lindsey et al. 2005). This is based on the principle that nature or biodiversity must pay for itself by generating economic benefits (Kiss 2004). Ecotourism supports biodiversity conservation and at the same time promotes sustainable local development (Ross & Wall 1999). In South Africa, ecotourism is part of the sustainable development agenda, and it is viewed as an instrument of empowerment for underprivileged communities as it provides employment for rural communities (Holden 2013). For instance, several protected areas, both public and private, have promoted joint economic initiatives whereby specific services and functions are outsourced to local communities (Honey 2003; Mahony & Van Zyl 2002; Myburgh & Saayman 1999).

However, the significance of ecotourism is underrated, mainly because of a lack of information on the financial and economic performance of the ecotourism operations of PPAs (Barnes & De Jager 1996; Child et al. 2012; Eagles 2003; Musengezi 2010; Porter, Ferrer & Aylward 2003). This could in turn lead to an under-representation of the significance of ecotourism within fiscal sectors of government. This, according to James, Gaston and Balmford (2001), could lead to a lack of understanding on the expenditure of biodiversity conservation and create a perception that conservation initiatives are unfeasible. As Eagles (2003) states, this could result in cutbacks in funding for conservation agencies. Given that funding for PPAs has been inadequate and declining over the years (Emerton, Bishop & Thomas 2006), research on the financial and economic performance of ecotourism operations of PPAs will justify an investment in PPAs. The shortfall in annual spending for PPAs in developing countries is estimated to be in the range of $1 billion and $1.7 billion, which could be influenced by distinctive management objectives and activities (Bruner, Gullison & Balmford 2004). Moreover, easily accessible PPAs face an increased threat of degradation and thus would require more investment. Because of their respective unique desirable features, some PPAs will generate more revenue than others, making cross-subsidisation inevitable. As PPAs get larger, budgets have remained static, and therefore management costs per hectare in such PPAs would normally be higher, especially in easily accessible PPAs located close to disadvantaged communities (Bruner et al. 2004; Emerton et al. 2006).

In South Africa, funding for Ezemvelo KZN Wildlife (EKZNW), KwaZulu-Natal's wildlife agency, has been reduced by provincial government (Khumalo & Molla 2012). Reduced funding coupled with low admission fees is creating financial constraints and undermine the capacity of the agency. Eventually, this will compromise biodiversity conservation and local development. Therefore, for conservation managers to find cost-effective ways of managing ecotourism operations, they will need empirical information on detailed evaluations of income and expenditure patterns of ecotourism operations. Measuring the financial performance of ecotourism operations in PPAs will enable EKZNW to control and improve operational practices in the respective PPAs.

There are several operational performance measurement methods that have been employed in the literature to measure efficiency, prioritise conservation and justify investment in PPAs in various contexts. The most important of these include appraisal methods such as cost-effective analysis (e.g. Laycock et al. 2009; Moran, Pearce & Wendelaar 1997), benefit-cost ratios (e.g. Dixon & Sherman 1991), cost-benefit analysis (e.g. Dixon & Sherman 1990) and so on. The various methods of comparing the costs and benefits of protected areas are summarised in Dixon and Sherman (1991). Data envelopment analysis (DEA) is a non-parametric method used to measure the relative efficiency of decision-making units (DMUs) (Charnes, Cooper & Rhodes 1978; Speelman et al. 2008). It has often been employed to assess the relative efficiency of DMUs of protected areas and to indicate how it could be improved by providing a set of guidelines (Bosetti & Locatelli 2006). Moreover, another method that imposes internal benchmarks to measure operational performance is total factor productivity (TFP), which is used to measure the changes in aggregate output per unit of aggregate input (Thirtle & Bottomley 1992). Nevertheless, these ratios and methods have various features that make it challenging to aggregate them to provide an understanding of the overall operational performance (Parkan 1996). Only time series data, unit prices and quantities can be used in the model to obtain performance measurements with TFP (Parkan 1996).

Operational performance can be used for comparative analyses, as explained by Ghalayini and Noble (1996). According to Parkan (1996), examining the operational performance of a firm overtime introduces managers to aspects of comparison between operations across time and thus competition. Therefore, in each PPA, ecotourism operations that consume fewer resources and generate higher revenues in a specific year would have performed better or more competitively relative to other years. This means that operational performance measures the relative competitiveness of ecotourism operations over a period (Parkan 1996). Therefore, his study aims to examine the operational competitiveness of ecotourism operations in each PPA managed by EKZNW. This is performed using a non-parametric method called operational competitiveness rating analysis (OCRA). This method was selected because, according to the authors' knowledge, it is the first of its kind in South Africa to use the OCRA procedure to measure and assess the operational competitiveness of PPAs and it has an advantage over other methods. The advantage of the OCRA model is its capacity to show the period during which EKZNW's overall operational performance has been inadequate or more than adequate compared to other methods as well as the sources of those shortcomings and strengths (Parkan 1996). This is possible because of the way operational competitiveness ratings (revenue generation and resource consumption inefficiency ratings) are computed and its ability to incorporate management's perceptions of the relative importance of the cost and revenue categories.

The article begins with a brief account of the competitiveness of PPAs, followed by the study area and data description. Then the OCRA method, the empirical model, is explained. Thereafter, the operational competitiveness profiles of each PPA are presented. This is followed by the presentation of the results and discussion on the operational competitiveness of ecotourism operations. Finally, conclusions and strategic implications are drawn.


Ezemvelo KZN Wildlife and public protected areas

The majority of PPAs are managed by central governing bodies or conservation agencies (Eagles 2002; Porter et al. 2003). These conservation agencies collect revenues from PPAs and allocate operating budgets (Eagles 2002). However, budget allocations are not closely linked with ecotourism use levels (Eagles 2002) and the environmental value of the area, which reduces the incentives of PPAs to manage their operations sustainably and profitably. This is true of EKZNW because part of its budget comes from government and the the rest from own revenue generated.

According to EKZNW management authorities, the current budget of EKZNW is around R890 million, of which 25% is generated from its own operations and the rest is a government subsidy. EKZNW retains all its revenues and submits a budget request to the KZN Department of Economic Development, Tourism and Environmental Affairs. The department often allocates its financial resources on a 3-year cycle to different sectors (including EKZNW) in the province, based on the availability of its financial resources and priorities. Furthermore, when there is a budget deficit, EKZNW will negotiate with the department for more funding, seek other funding sources or attempt to increase its internal sources of revenue. However, if these options cannot make up the budget shortfall, EKZNW will typically reduce its budget for lower priority areas.

Still, increasing budget cuts from government have affected the capacity of EKZNW to cover its costs. EKZNW manages several protected areas of which some have ecotourism features that generate sufficient income whilst others have biodiversity value that lack income-generating features. Therefore, income from profitable protected areas with ecotourism features is used to cross-subsidise those with pure public good features in terms of biodiversity conservation. This has made it necessary for EKZNW to prioritise projects in its PPAs according to their ability to generate revenue.

There have been frequent demands by the South African government for EKZNW to design and implement a strategy that is aligned with current trends on sustainable funding for protected area management, in which there is a need to balance between biodiversity conservation objectives and revenue generation (Dube 2011). The reasons for this request range from poor corporate governance, recurrent financial mismanagement and pressing socio-economic development needs (Dube 2011; Ridl 2012). EKZNW is considering new business models aimed at achieving business efficiency by optimising the use of financial resources and increasing its resource base (EKZNW 2009). Furthermore, EKZNW plans to focus on more effective marketing strategies to increase revenues (EKZNW 2009). According to Dube (2011), for EKZNW to reduce its dependence on government finance, it needs to focus on three areas, namely, payment for ecosystems services, public-private partnerships and co-management with the private sector and communities.

Moreover, several PPAs in KZN and most of South Africa typically generate insufficient revenues to finance operations and cover costs, and as such, most are managed at a loss (Dube 2011; Myburgh & Saayman 1999). According to officials at EKZNW, PPAs are not mandated to aim for profit even though it is desirable to at least cover their full costs. Typical revenue sources for most PPAs, including the EKZNW PPAs, include accommodation, wildlife product sales, admission fees, rentals and concessions, and wildlife viewing (Eagles 2002; Parker & Khare 2005; Porter et al. 2003). According to Dixon and Sherman (1991), there are three types of costs in maintaining protected areas: direct costs (recurrent costs of maintaining and managing a protected area), indirect costs (damages caused by wildlife) and opportunity costs (foregone losses resulting from protecting such areas). The benefits and costs of ecotourism interact in complex ways, but it is imperative that PPAs maximise their benefits whilst minimising costs (Eagles et al. 2002).

Several PPAs in KZN and most of South Africa generate insufficient revenues to finance operations and cover their running costs (Dube 2011; Myburgh & Saayman 1999). There have been studies conducted on PPAs in KZN that have touched on the theme of performance or competitiveness in ecotourism (e.g. Flanagan 2014; Porter et al. 2003). Internationally, for instance, a report by Bovarnick et al. (2010) was compiled for the United Nations Development Programme (UNDP) to analyse the financial status and sustainability of protected areas in several Latin American and Caribbean countries. This report found that protected areas in these regions are underfunded and that funding needs are likely to increase considering the implications of climate change. Moreover, Rylance (2017) conducted a study to assess the revenue generation of tourism in 93 Mozambican protected areas. The findings of the study were that the total annual revenue generation from protected areas was $24m from tourism-related activities, contributing around 10% to the tourism sector. Accordingly, identifying and reporting all possible revenue flows will assist in justifying public support for protected areas. However, the literature on this subject in South Africa is very limited, particularly on operations cost estimation and assessment. Therefore, to contribute to this body of knowledge, this article will examine the allocation of resources or funds by EKZNW to ecotourism operations in PPAs and the financial performance or competitiveness of these operations relative to each other over time.


Study area

The KZN province covers an area of 92 285 km2, and is situated on the eastern coast of South Africa in a biologically rich transition zone between tropical biota in the north and subtropical biota in the south (Eeley, Lawes & Piper 1999; Goodman 2003). The abundance of KZN's biodiversity comes from its altitudinal gradient and its varied geology, topography and climate (Eeley et al. 1999; Goodman 2003).

EKZNW is a government agency under the KZN Department of Agriculture, Environmental Affairs and Rural Development and manages biodiversity conservation in the KZN province (Goodman 2003). EKZNW was created in 1997 via a merger between the Natal Parks Board and the KwaZulu Department of Nature Conservation (Goodman 2003). It manages 110 PPAs that cover over 675 000 hectares (Aylward & Lutz 2003). Moreover, it receives financial support from government through the KZN Provincial Treasury (Dube 2011; Goodman 2003). Furthermore, the agency receives additional funding from non-governmental organisations (NGOs) and philanthropic organisations (Dube 2011). Protected areas considered in the study are shown in Figure 1.



The data

To evaluate the trends in the competitiveness of commercial operations in PPAs, financial data were collected from EKZNW for 2007-2013. Originally, the PPAs required were to be selected using the stratified random sampling strategy across all EKZNW administrative regions (uKhahlamba, Zululand and coastal regions), where between 35 and 50 PPAs mainly focused on ecotourism operations would have been selected. However, EKZNW was only able to provide 32 randomly selected PPAs from the 110 PPAs, some with a stronger conservation focus than a commercial operation, citing organisational privacy concerns. EKZNW provided PPAs selected randomly across uKhahlamba, Zululand and coastal regions with 14, 7 and 11 PPAs, respectively. Therefore, there is a reduced possibility of sample selection bias in the study.

The financial data provided consisted of annual cost and revenue values of commercial operations. Cost and revenue values for each protected area were in nominal terms. Hence, the South African consumer price index was used to deflate the cost and revenue values for 2007-2013, taking 2005 as the base year. Cost and revenue items for each protected area were disaggregated and measured separately. In this study, cost categories are described as resources consumed and revenue categories as revenues generated. Thus, 11 cost categories and seven revenue categories were analysed per year, 2007-2013 (Table 1).



Empirical model: The operational competitiveness rating analysis procedure

Operational competitiveness rating analysis is a relative performance method used to measure the performance of operating entities called production units (PUs). PUs are purposeful entities that convert resources into goods and services (Parkan 1991). The OCRA procedure involves simple ratio-type, non-iterative computations that measure the PUs relative to operational competitiveness (Parkan et al. 1997). It is suitable for time series data: financial value, on quantities and unit prices, and has been used in different industries to measure operational competitiveness (Parkan 1996, 1999, 2003; Parkan et al. 1997; Parkan & Wu 1999).

Following Parkan (1996), the model can further be described as follows. In the model, an operating entity is represented by a PU in each year. A comparison is conducted of operational performance of k PUs that consume resources in C categories and generate revenues in the R categories. To compute cost and revenue inefficiency ratings, the prices and quantities of inputs and outputs could be used to obtain information about k PUs' relative input or output efficiency. Nonetheless, resource cost and revenue values can be used to obtain relative cost and revenue inefficiency ratings, respectively (see Parkan 1996 for details on derivation). In this study, because data on only cost and revenue values were used, the kth PU's cost and revenue vectors can be represented as vectors , respectively, with as the cost incurred for the ith resource and as the revenue generated from the jth output. Cost and revenue at PUk are denoted as and , where Ckm = costkm = 1ukm is the cost of the mth resource category, m = 1,……, M, and Rkh = revenuekh = 1vkh is the revenue generated from the hth category of outputs, h = 1, , H, at PUk, k = 1,., K.

According to Parkan and Wu (1999), the relative importance of a PU's performance in a cost or revenue category is dependent on that category's impact on the overall performance of the PU. This relative importance is reflected by calibration constants, denoted as akm and bkh for resource consumption and revenue generation, respectively, for PUk. The calibration constants can be computed by:

The first equation defines am as the average cost share of the mth cost category, and the second equation describes bh as the average revenue share of the hth revenue category (for details, see Parkan 1996).

The resource consumption inefficiency model is meant to determine whether input quantities would give information about a PU's relative input inefficiency. The kth PU's resource inefficiency rating, Ckm, is computed with respect to the mth input category and is expressed as:

where the is the lowest cost incurred by a PU amid K PUs with respect to the mth cost category. Then, the sum is linearly scaled by:

which is the kth