Primary data collection

The type and structure of primary data collected from the stakeholders are depicted in the questionnaires and the questions asked during the interviews (Appendices, A, B, C and D). The specifics on the data collected and analysis are presented in the methods and material section of each chapter from 4 to 7. Additional data (for appendices A, B and D) was collected to ensure that the research objectives are satisfactorily fulfilled and research questions are adequately answered. The questions for additional data are depicted in the appendices.

3.3.1.1 Residential households

The data from the residential households were collected in nine provinces across South Africa. Two random sampling methods were used for the residential settlement/electricity consumers (data sources):

 Stratified random sampling

Stratified random sampling was used to select the sample in order to give every individual (residential households) in the population (country) an equal chance of inclusion in the sample. It is a sample method that involves the division of a population (country) into smaller groups known as strata. For this study, the strata are referred to as provinces. Consequently, the population (country) was broken down into nine (strata) provinces (Figure 3.3).

125  Systematic random sampling

Systematic random sampling was used to complement the stratified random sampling in this study. It is a sample method that yields a more representative sample than a simple random one, especially when the sample size is small (provinces), and needs to be broken down into district areas within the provinces (Figure 3.3). It seeks to eliminate sources of bias. Systematic sampling is a sampling method in which sample members from a larger population are selected according to a random starting point and at a fixed periodic interval.

The systematic random sampling yields more accurate results than simple random sampling. This interval depends on the number of population, for instance, in the provinces with 5 districts, the interval could be 2, meaning that after every second district the third one is chosen. In this research, the district municipal areas were listed and every third district municipal area in each province was selected. Then two districts in each province were included in the sample, for example in Limpopo Province, the Capricorn and Vhembe districts were selected for data collection in this study (Figure 3.3).

A team was established in each province for data collection. The team was comprised of students chosen from the nearest University or College, mainly in the Faculty/Department of Natural Sciences, Environmental Sciences or Electrical Engineering as the understanding on the subject is anticipated to be better than students from other Faculties. Each team was comprised of four students. Therefore, a total of 36 students participated in the residential household data collection in nine provinces across the country. This is believed to have benefited students as they learnt skills on the field data collection and it was one way of preparing them towards their postgraduate studies.

The students were given training by the researcher. The training took place prior to field data collection. The training included the following aspects:

 Community members general behaviour during data collection;  Understanding of solar PV;

126  Electrical appliances;

 How to fill in questionnaire;

 How to handle difficult community member; and

 Suppliers of electricity in South Africa (which is Eskom and some Municipalities). The decision to get students in the nearest University was for two reasons; namely: 1) the students are able to communicate with the residents using the local language, hence the communication becomes better and easier for the residents to understand the questionnaire during data collection; and 2) local students are familiar with the villages/locations around, hence it would be easier to approach community members. In the field data collection, there is an element of errors and biasness in the first few days of data collection. However, as data collection continues the experience and confidence of field workers grow up and errors and biasness may reduce or stop, if ever they were there in the first place. In order to check, verify and validate the data; the first 50 questionnaires were compared to the last 50 questionnaires to check the variation and consistency. This helped to ensure that there are no mistakes, errors or biasness from students.

In rural areas, the Headmen were approached by the researcher and the data collection team requesting permission to collect data in their villages. A letter from Newcastle University and researcher’s university student card were presented to the Headmen for credentials and legitimacy of the project (Appendix F). There are some villages where the data was collected on Sundays. This was because the Headmen would have called for the meeting with all residents in the village to communicate matters concerning the village, thus the data collection team waited until the meeting came to an end, then collected data from members of the community. Some residents, especially senior citizens or pensioners were unable to write; in this case, the data collection team members were asking questions and filling in the questionnaire on their behalf.

Apart from team of students that collected data from the residence using hardcopy questionnaire, there was an electronic questionnaire that were sent to the residents through e-mail using the ‘SurveyMonkey’ website (www.surveymonkey.co.uk). The

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questionnaires were sent to the members of community in the same district municipalities across the country. The e-mail addresses were collected from the provincial government departments, municipalities and private organisations that are based in the district areas selected (Table 3.2). The communication divisions were approached and a request for e-mail database was made. The letter from Newcastle University (Appendix F) and a student card were produced for verification by the organisations before the release of e-mail database.

The e-mail addresses were collected and a database was established where a link on the ‘SurveyMonkey’ data collection website was sent through to the respondents. The response turnover was not great at first, reminder e-mails were sent four times and in the end, approximately 70% responded though. The breakdown of responses per reminder was that approximately 20% responded after the initial e-mail with

‘SurveyMonkey’ link for questionnaire. The first reminder e-mail was sent and 40%

responded, 20% responded after the second reminder e-mail, 15% responded after the third reminder e-mail and 5% responded after the fourth reminder e-mail.

The main challenges experienced during data collection on a house to house and

‘SurveyMonkey’ were as follows:

 Respondents took too long to complete questionnaire, reminder e-mails were sent four times, and approximately 70% of the respondents completed the questionnaire;

 Respondents did not like questions in which they are supposed to explain, rather prefer questions that they would put tick in a box;

 Some respondents were not willing to complete the questionnaire;

 Some respondents asked for money in exchange for completing questionnaire;  In some houses there were dogs which threatened to bite the data collection

team members;

 Some respondents said the questionnaire is too long;

 Some respondents did not know their electricity supplier because they are not the ones that pay the bill;

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 Many respondents did not like the household income category (low, medium to upper) question, they deemed it too personal and private. Therefore, it was subsequently removed from the questionnaire.

The questionnaire used for data collection at the residential households is appended in Appendix A.

Figure 3.3: Villages and towns that data was collected in two districts from each province

Thirteen thousand (13000) questionnaires were completed across the country in two district municipalities in each province. There were 60% questionnaires for the villages and informal settlements, which totalled 8800 from 18 district municipal areas, where data was collected though hardcopy questionnaires. On average each student collected approximately 250 data questionnaires.

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The remaining 40% was collected from the cities and suburbs, which is 4200 (this is 70% of the questionnaires sent through in e-mails). The questionnaire was sent to a total of 6000 e-mail addresses, and 4200 responses were received, with 1 800 responses that were not received, despite all the reminders. More information on the province, municipal areas and number of questionnaires completed in each municipality is detailed in table 3.2. On average a total of 720 questionnaires were completed for each municipal area (Table 3.2).

The establishment and training of data collection teams in each province took place from January – March 2014. The data collection started in April 2014, preliminary data was analysed in June 2014 in preparation of the first year viva examination. The data collection and capturing continued from September 2014 to March 2015. This was because in other provinces such as Northern Cape and Kwazulu Natal the responses were low, and from mid-October to early December the students were writing their examinations.

Table 3.2: Locations where data was collected and number of questionnaires collected

Province Municipality Villages/Towns

where data was

collected

University/College that students are

based (for hardcopy questionnaires) Number of questionnaires completed

Limpopo Vhembe Thohoyandou Shayandima Tshino Vyeboom University of Venda ₁₂₀₀ Polokwane Polokwane Chuenespoort Ga-Mothiba University of the North 850

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Province Municipality Villages/Towns

where data was

collected

University/College that students are

based (for hardcopy questionnaires) Number of questionnaires completed Mogale Kagiso Krugersdorp

West Rand College ₆₂₆ Mpumalanga Nkangala eMalahleni

Ogies Masakhane Tshwane University of Technology- Nelspruit Branch 580

Gert Sibande Ermelo Camden

Gert Sibande FET College

450

Kwazulu Natal Amajuba Newcastle Ngagane

Majuba FET College ₈₃₅ Umgungundlovu Richmond

Ndaleni

Umgungundlovu FET College

480

Eastern Cape OR Tambo Mthatha Nyandeni

Walter Sisulu University

756

Alfred Nzo Mbizana Dumasi

Walter Sisulu University

790

Western Cape Cape Town Khayelitsha Gugulethu

Cape Peninsula University of Technology

855

Central Karoo Laingsburg South Cape College ₄₈₀ Northern Cape Francis Baard Kimberly Northern Cape

Urban College

890

Siyanda Postmanburg Northern Cape Urban College

587

North West Bojanala Rustenburg Brits Springfield College ₇₈₉ Dr Kenneth Kaunda Ikageng Potchefstroom

West Rand College ₆₁₃ Free State Mangaung Thabanchu

Botshabelo

Central University of Technology

713

Fezile Dabi Kroonstad West Rand College ₆₃₃

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3.3.1.2 Solar PV companies

The South African Photovoltaic Industry Association (SAPVIA) is an organisation whose members are active in the solar PV technology sector. The SAPVIA is a non- profit association representing members largely made up of developers, manufacturers/installers and service providers operating within the PV industry. The association is devoted to promoting the growth of South Africa’s solar PV electricity market and representing the industry to provincial and national Government.

A list of solar PV companies was obtained from SAPVIA. The systematic sampling was used to select the companies for data collection through interviews. The list comprised 28 companies. The data to be collected for this research involve various sectors such as residential, generators, government and solar PV companies for the primary data. Moreover, there was also a secondary data to be collected for the modelling. Therefore, the researcher decided to cover at least 20% of the solar PV companies received from SAPVIA instead of interviewing all 28 companies due to volume of data collection for all sectors. The interval was three, meaning that every third company in the list was chosen (Appendix B). Then out of 25 companies, 9 were chosen and interviewed. The 9 companies represented more than 20% of the total sample size. This method yielded a more representative sample than the random sample.

All nine companies were interviewed. Seven companies are based in Gauteng Province with satellite offices in other provinces where they have solar PV projects. The researcher went to their offices where the interview meetings took place. The other two companies are based in Durban. Telephonic interviews were conducted and 5 companies requested the results of the study when the report is completed. The company representatives that were interviewed were the Business Development Managers in seven of the companies, whilst in the other two they were technical personnel that are involved in projects on the site. Not all the companies were comfortable with the questions around the cost of their PVs and proportion of their customer base (residential, commercial, mining etc.). The reason cited was confidentiality, fear that their competitors might unfairly, and unlawfully use the information to attract customers, even though the researcher explained that data given

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is confidential and will neither be divulged to other parties nor used for other things except for research work. The prices given on the questions asked was therefore a general price, but not company specific prices.

The challenges experienced were to secure the appointment for interviews. Most of the Business Development Managers seemed to be extremely busy with meetings after meetings day in day out. The researcher made several follow-ups until the meeting took place for data collection. Similarly, for the two companies in Durban it was difficult for the company representatives to be available for telephonic interviews.

3.3.1.3 Government

The government departments that deal with energy matters were identified. Four departments were approached by the researcher and interviews were arranged. The researcher presented the letter (Appendix F) and student card to confirm the credentials and legitimacy of the project. The departmental spokesperson identified the relevant Senior Manager for the interview. Then the researcher arranged a meeting directly with the representative of the department.

The interviews took place and representatives in all departments were helpful and answered all the questions (Appendix C). The departments that were interviewed are:

 Department of Energy (Energy policies, strategies and legislation);

 Department of Science and Technology (Energy research, development, and technology innovation);

 Department of Trade and Industry (Import and export of energy resources and equipment); and

 National Energy Regulator of South Africa (Energy tariffs determination and approval).

There were no challenges encountered with the government sector regarding data collection. The departments gave their best cooperation and there was an interest regarding the research findings.

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3.3.1.4 Generators

The South African electricity sector comprises the national utility (Eskom Holdings) that generates over 90% of the total electricity production in the country. The remaining less than 10% comes from the municipalities and private generators. Therefore, data was collected from Eskom Holdings and other smaller generators (Table 3.3).

Table 3.3: Electricity generators from different energy sources

Generator Capacity (MW) Power source

Eskom 42 000 Coal, Gas, Oil, Nuclear,

Hydro, Wind

City of Tshwane 480 Coal

Bethlehem Hydro 7 Hydro

Kelvin power station 420 Coal

Darling wind farm 5 Wind

The data from Eskom Holdings was collected in April 2014, and for the other generators was collected in October and November 2014. The data for Eskom was collected in two separate meetings in a form of discussion and completing questionnaire, the reason for two meetings was that in the first meeting the Eskom representative did not have all the facts and figures as the company is big with over 40000 MW power generation. A questionnaire (Appendix D) was sent to the other three generators for completion.

There were no challenges experienced in data collection from the generator except that the researcher had to make follow up for the questionnaire to be completed by the three generators (except Eskom Holdings).