The main aim of this paper is to evaluate the growth of direct full-time employment (FTE) opportunities created from different types of renewable energy in Australia from 2009-10 to 2016-17. The key research question of the study is: what are the differences in the employment growth number between various renewable energy types categorized by its groups in Australia from 2009-10 to 2016-17? Findings from this paper will help understand the employment growth trend in Australia’s renewable energy sector over the studied period and in making future predictions.
The obtained data in this study presents statistics of direct FTE employment in renewable energy activities in all the eight states of Australia from the year 2009-10 to 2016-17. Data for FTE employment by renewable energy type in each state has also been provided.
Data analysis entails organizing the collected data so as to help the researcher answer his research question and make a clear conclusion about the study. This is a quantitative study hence the data will be analyzed using quantitative data analysis techniques. Quantitative data analysis methods include: regressions, frequency distributions (use of histograms), descriptive statistics (mean, mode, median, standard deviation, variance, etc.) (Ali & Bhaskar, 2016), statistical testing (such as T-tests), cross-tabulation (such as use of pivot table in Excel), correlations and text analytics, among others. The data analysis method to be used in this study is regression.
Regression models are used to establish the relationship among variables. In this study, renewable energy activities in Australia (including renewable energy types) is the independent variable while the number of direct FTE jobs is the dependent variable. Regression analysis also helps the researcher to understand the extent that the dependent variable will change when any of the independent variables is changed, while holding other independent variables constant. A regression function will be determined. The regression function is usually in the form:
Where Y = dependent variables, X = independent variables and β = unknown parameters that can either be a vector or scalar.
The function in equation 1 above can be used to predict the number of direct FTE jobs that will be generated in Australia in the future based on the investment amount in different types of renewable energy projects. The regression function can either be linear or non-linear (polynomial, logistic, stepwise, lasso, ridge, cox, passion, elastic net regression) (Otwombe, Petzold, Martinson, & Chirwa, 2014); (Ray, 2015). The regression analysis in this study will be performed in Excel and the following regression statistics are expected to be obtained: multiple R, R square, adjusted R square, and standard error. Reliability or statistical significance of the results will also be checked by looking at the value of significance F. The results will only be termed as reliable if the value of significance F is less than 0.05. Otherwise, the set of independent variables used will be changed (those whose P-value is greater than 0.05) and the regression rerun until the significance F value drops below 0.05.
A study by Jennings (2009) showed that renewable energy sector has created numerous employment opportunities, both directly and indirectly. The researcher noted that most professionals in the energy sector, including engineers, energy planners and scientists, do not have adequate training on how renewable energy technologies work. Thus for the country to achieve its renewable energy targets, more personnel must be trained. Nevertheless, the author of the article did not provide any figures shows the employment growth trend of renewable energy activities.
A report compiled by Climate Council of Australia (2016) showed that all states across Australia will experience net job growth if Australia attains the target of ensuring that 50% of total energy is derived from renewable sources by 2030. According to this report, if 50% renewable energy production is achieved by 2030, extra 28,000 new will be created, approximately 50% more jobs than if renewable energy production in the country remains business as usual (about 34% in 2030). The report’s findings also showed that new jobs created in the renewable energy sector will not be adequate to compensate jobs lost in fossil fuels electricity generation. Proper models and technological tools should be used to create simulations showing how employment prospects in the energy sector change when certain parameters are varied.
Henriques, Coelho, & Cassidy (2016) carried out a study to determine the impact that renewable energy production targets had on employment across Europe from 2008 to 2020. The researchers used input output approach to conduct their study. Findings from the study showed that deployment of renewable energy technologies creates employment opportunities for people with different levels of knowledge. The number of jobs created is also varied across different types of renewable energy and the lifecycle stages of renewable energy. This study shows that as more resources are channeled towards deployment of renewable energy technologies, more jobs are also created in the sector.
There are numerous co-benefits of renewable energy generation, including creation of direct and indirect employment (Pittock, 2011). For many years, fossil fuels energy generating plants have been developed in urban centres where energy demand is high. This has left remote areas underdeveloped due to lack of electricity. However, this may change with deployment of renewable energy technology. Remote Australia has abundant renewable energy resources that can create employment and other income-generating opportunities for local residents. Therefore the government can create more jobs by capitalizing on the renewable energy potential in both urban and rural areas.
According to Thomas, Jennings, & Lloyd (2008), renewable energy education is one of the most essential elements that promote exploration and implementation of renewable energy systems. As the country increases its renewable energy production, the need for more researchers, scientists, engineers, consultants, designers and other more informed experts to provide information and train others about renewable energy systems has also gone up. However, this article did not provide real values showing the purported growth of employment opportunities in renewable energy sector.
As noted by Lambert & Silva (2012), deployment of renewable energy systems is promoted in various countries so as to boost energy independence, mitigate climate change and create more jobs. The two researchers conducted a study to establish factors that should be considered when analyzing the impact of renewable energy on employment. These factors included: labour intensity of renewable energy types, job losses, assumptions of models used, job skills and quality, increases of costs and investment availability and sources of data. They concluded that analytical studies (such as use of surveys) are better suited for regional studies whereas input-output approaches are more suitable when conducting national and international studies. This shows that employment growth of Australia’s renewable energy activities should be analyzed using input-output methods.
As identified by Dvorak, Martinat, Van der Horst, Frantal, & Tureckova (2017), investments made in renewable energy sector mainly target to achieve the following: energy security, environmental conservation, job creation and boost economy. The researchers conducted a study to evaluate employment benefits attained from renewable energy investments in Czech Republic between 2008 and 2013 and compared the data with those of European Union countries. Findings from the study showed that a significant number of jobs (0ver 20,000 jobs in 2010) were created in Czech Republic’s renewable energy sector and this number was largely influenced by protraction of financial incentives. However, it is also important to consider how a country’s competitive advantage in renewables affects job creation.
One of the socio-economic impacts of renewable energy investments is creation of jobs (Sastresa, Uson, Bribian, & Scarpellini, 2009). A group of researchers carried out a study to establish the direct jobs created in a local area (Aragon, Spain) as a result of investments made in renewable energy sector. Several parameters were considered during the study, including: number of jobs created, quality of jobs created, per capita income, technological development, human capital and territorial development. Findings from the study showed that renewable energy investment creates numerous jobs in an area thus boosting social and economic ecosystems of the local residents. Nevertheless, it is also important to consider the sustainability of jobs created by renewable energy systems.
Garrett-Peltier (2017) states that green energy systems (renewables) create more FTE jobs that brown energy systems (fossil fuels). The researcher conducted a study to assess employment impacts of brown and green energy. The author’s main aim was to determine if the number of jobs created in the clean energy sector is more than the number of jobs lost in the fossil fuels sector. Results obtained from the study showed that every $1 million that is changed from being spend in brown energy sector to green energy sector creates a net increase of five FTE jobs. Thus if the amount of money being spend in Australia’s fossil fuels sector is completely shifted to renewable energy sector, job opportunities in the country will increase by approximately 70%. However, the author did not state the assumptions or requirements that must be met for the projected number of jobs to be created in renewable energy sector.
In a study conducted by Markandya, Arto, Gonzalez-Eguino, & Roman (2016), about 530,000 jobs were created in the European Union (EU) during energy transition period that took place in the region between 1995 and 2009. This study shows that transformation in the energy sector from non-renewables to renewables has significant effects on employment prospects. But the authors of this article combined employment statistics for both renewables and gas. It would have been better if they narrowed their scope to renewables only because these are the green or clean sources of energy, and they are what many countries are focusing on now.
Since this is a quantitative research, numerical data has to be collected and analyzed so as to answer the research question adequately. A quantitative research method is used when the researcher wants to explore the relationship between different variables. This is done by using numerical data, mathematical models and statistical methods. The researcher will not be collecting data afresh (primary data) but will use existing data (secondary data). Therefore the proposed method of data collection in this study is records and documents. The case study in this research is Australia.
The research will collect data from existing records kept by one of the Australian institutions – Australian Bureau of Statistics (ABS) databases. ABS is Australian government’s official and independent statistical organization hence it is believed that the records it has are accurate.
The secondary data will be collected from the following link available on ABS website: https://www.abs.gov.au/ausstats/[email protected]/mf/4631.0. The data available on this link was last updated in April 2018.
To help the researcher have a clear roadmap on how to conduct the study and complete it on time, a project schedule has been prepared. The schedule comprises of the tasks that will be undertaken in the study and the period within which they will be completed. The Gantt chart schedule is as provided in Table 1 below
Table 1: Gantt chart schedule
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Tasks |
Period |
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|
8/20 |
8/27 |
9/3 |
9/10 |
9/17 |
9/24 |
10/1 |
10/8 |
10/15 |
10/22 |
10/29 |
11/5 |
|
Prepare research proposal |
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|
Submit proposal |
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|
Revise research proposal |
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|
Submit final proposal |
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|
Evaluate data collection and analysis methods |
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|
Data analysis |
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|
Prepare final report draft |
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|
Submit final report draft |
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|
Revise final report |
||||||||||||
|
Submit Final report |
||||||||||||
|
Prepare report presentation |
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|
Final report Presentation |
From the timeline in Table 1 above, the whole project will be completed in less than three months. The project will be done in 11 weeks. Since the data is already available, no time will be spent on data collection, which usually takes the largest percentage of a research project like this one. The cost of completing the study is also very minimal. Considering the nature of activities to be performed and the time allocated for each activity, it is very practical to complete the project within the timeline provided in the Gantt chart above.
References
Ali, Z., & Bhaskar, S. (2016). Basic Statistical Tools in Research and Data Analysis. Indian Journal of Anaesthesia, 60(9), 662-669.
Climate Council of Australia. (2016). Renewable Energy Jobs: Future Growth in Australia. PPotts Point, Sydney, Australia: Climate Council of Australia Ltd.
Dvorak, P., Martinat, S., Van der Horst, D., Frantal, B., & Tureckova, K. (2017). Renewable energy investment and job creation; a cross-sectoral assessment for the Czech Republic with reference to EU benchmarks. Renewable and Sustainable Energy Reviews, 69, 360-368.
Garrett-Peltier, H. (2016). Green versus brown: Comparing the employment impacts of energy efficiency, renewable energy, and fossil fuels using an input-output model. Economic Modelling, 61(1), 439-447.
Henriques, C., Coelho, D., & Cassidy, N. (2016). Employment impact assessment of renewableenergy targets for electricity generation by 2020—An IO LCA approach. Sustainable Cities and Society, 26(1), 519-530.
Jennings, P. (2009). New Directions in Renewable Energy Education. Renewable Energy, 34(2),435-439.
Lambert, R., & Silva, P. (2012). The challenges of determining the employment effects of renewable energy. Renewable and Sustainable Energy Reviews, 16(7), 4667-4674.
Markandya, A., Arto, I., Gonzalez-Eguino, M., & Roman, M. (2016). Towards a green energy economy? Tracking the employment effects of low-carbon technologies in the European Union. Applied Energy, 179(1), 1342-1350.
Otwombe, K., Petzold, M., Martinson, N., & Chirwa, T. (2014). A Review of the Study Designs and Statistical Methods Used in the Determination of Predictors of All-Cause Mortality in HIV-Infected Cohorts: 2002–2011. PLoS ONE, 9)2), 1-15.
Pittock, B. (2011). Co-benefits of large-scale renewables in remote Australia: Energy futures and climate change. The Rangeland Journal, 33(4), 315-325.
Sari, A., & Akkaya, M. (2016). Contribution of Renewable Energy Potential to Sustainable Employment. Procedia – Social and Behavioral Sciences, 229, 316-325.
Sastresa, E., Uson, A., Bribian, I., & Scarpellini, B. (2009). Local impact of renewables on employment: Assessment methodology and case study. Renewable and Sustainable Energy Reviews, 14(2), 679-690.
Thomas, C., Jennings, P., & Lloyd, B. (2008). Issues in Renewable Energy Education. Australian Journal of Environmental Education, 24(1), 67-73.
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