News - Community Surveys

TV Energy has recently completed two community surveys to understand public opinion about renewable energy.

OSNEY ISLAND RESIDENTIAL SURVEY

Results of the Osney Island survey show that local residents support renewable energy and most would like to be involved in a community renewable energy scheme.

The Osney Sustainable Island Group received a Community Renewables grant from TV Energy for £1000 and funding from the South East England Development agency to pay for a feasibility study to investigate hydro potential for Osney weir and to conduct an inclusive residential survey to gather information about key environmental issues and renewable energy.

Sampling

The sample size was the entire population of 285 households, considered to be a manageable sample giving maximum coverage of residents' views and opinions.

Questionnaire

The questionnaire consisted of general awareness questions about environmental issues designed to measure knowledge levels, more specific questions about renewable technologies and whether these should be increased, attitudes and opinions about different types of renewable technologies and demographic information about the householder.

Data Collection

An announcement appeared in the December Osney residential newsletter that a residential survey was to take place. Members of the Osney Sustainable Island Group and other volunteers visited each house on the island and asked householders if they would be willing to take part in the study. Survey forms were then collected in person at a later date. The response rate was about 30%.

Data Analysis

Several areas were explored for analysis. Levels of knowledge were computed using frequency distributions and bivariate analysis was carried out to explore associations between variables. A single variable was then created from the sum of all knowledge variables which showed high levels of awareness for key environmental concepts. Further analysis revealed that those aged between 35 and 44 demonstrated the highest level of knowledge and cross tabulations showed that those who were more concerned about climate change had greater knowledge of environmental issues.

Similar statistical analysis was computed to measure whether respondents wanted to increase the use of renewable energy and this yielded an overwhelming response of 99% with the majority believing that wind, solar and biomass could at least partially replace fossil fuels. Awareness of green energy tariffs and government grants were low but those who were renovating property would be keen to install renewable technologies if payback was within five years and these figures were raised if the respondent had known someone else with renewables.

Finally support for renewable technologies revealed an average of 85% from residents and this figure was replicated in the enthusiastic response for participation in a community renewables project. This should put Osney Island residents in a favourable position to campaign for and implement renewable energy on the island. For the full survey results click here.


READING AREA STUDY

Preliminary results show that Reading people are keen to increase renewable energy in their region.

The Reading Area Study was an important public opinion survey and a useful information gathering exercise to explore attitudes and perceptions of an urban population towards environmental awareness, energy efficiency and support levels of renewable energy. The study was part of a series of data collection procedures that focus on individual and organisation choices related to renewable energy acceptance and procurement in the South East of England. As far as it is known the survey was the first of its kind in Reading that attempted to introduce an integrated and rigorous approach in its reportage rather than just revealing percentages or frequencies of response.

Sampling

A specific population of the Reading Borough was targeted, taking households as the unit of analysis as opposed to commercial properties. The sample size was calculated from a total population estimated to be 140,000. An ultimate sample size would be 700 minimum, given 80/20 split of the population, 5% standard error, but we increased the sample to 1000 which was in keeping with other industrial surveys so that the results could be used for more accurate comparisons.

The sampling frame was created through an area probability type sampling technique designed as a multi-stage strategy through which the population could be defined geographically. Our method grouped the Reading population by election ward, allocating the units to be surveyed by calculating the percentage of households from the total number and multiplying this figure by the total sample (1000). More heavily populated wards were surveyed more than those with sparser populations.

Data Collection

The procedure for data collection was decided after much discussion of the merits and inadequacies of mail, telephone and face-to-face interviews to be the household drop-off method. This is carried out by using a researcher to visit all households to be surveyed, leaving the survey to be completed at the respondent's leisure with collection at an agreed later date.

Questionnaire

This was generated from a series of closed an, pre-coded questions with a mixed question format. Sections contained general awareness questions on environmental issues, more specific questions about renewable energy, behaviour towards energy efficiency, attitudes and opinions regarding renewable energy support and some general demographic information.

Data Collection

Students from the University of Reading were recruited to deliver and collect questionnaires to householders. This followed briefings regarding the nature of the survey, how to approach and incentivise people to take part in the survey and not to be too demoralised when people refuse to take part. All but two wards had good rates of response and these were redone after students reported problems in achieving the numbers specified for distribution.

Data Analysis

Collected data was entered into a SPSS spreadsheet and cleaned for errors and missing values before analysis took place. Representativeness was calculated by comparing the survey data from the 2001 Census and results from this showed that the survey was closely comparable yielding a representative sample.

Level of Support for Renewable Energy

Analysis explored variables that measured the level of support residents reported towards the adoption of renewable energy. This was computed by running frequency tables, bivariate analysis and cross tabulations. Results showed an overwhelming response towards an increase in renewable energy and most considered this to be the responsibility of the national government. Favourable responses came from those who were keen to install solar PV/solar hot water systems in their homes and this was reflected in the high levels of support for solar and wind energy. Biomass support was low, but this could be due to lack of understand rather than opposition and other variables such as whether wood fuel could replace fossil fuels was also poorly reported. Having said that, people who reported support on one variable also supported others, creating a normal distribution and this is what we would expect to see.

To find out whether a particular type of person was more likely to support renewables was more difficult to extract, yet those of middle age range 36-50 were more supportive and those with a higher level of education also showed greater support. Bivariate correlations were computed to look for similarities and relationships between these variables and there was a statistically significant positive correlation between the technologies of wind energy and solar.

More causal relationships need to be analysed by using regression models. Initial results of these showed that older women were less supportive of men by age indicating and uncertainty the older they become, but this is not conclusive evidence. Further regression models will demonstrate if any other patterns emerge and further factors may be taken into consideration such as the network effect by which if one person knows another with renewable technologies they will be more likely to adopt it themselves. For the full report click on Task 29 Streatley Paper.