Enhancing the Renewable Energy and GDP Share of Agriculture in Indonesia: Analysis of the Impacts on Agricultural Sector Employment

Abstract

Renewable energy capacity, agricultural sector GDP, and agricultural employment in Indonesia experienced a downward trend from 1990 to 2019. This trend negatively impacts Indonesia’s target to utilize renewable energy as its energy source, employment in the agricultural sector, and the importance of agriculture to the Indonesian economy. Based on these issues, this research examined the impact of renewable energy usage and agricultural sector GDP on the employment rate of the agricultural sector by implementing the Autoregressive Distributed Lag (ARDL) model. According to empirical findings, total renewable energy supply improves agricultural sector employment in the short and long run; however, agricultural sector GDP improves employment only in the short run and negatively impacts the long run. It suggests that renewable energy has the potency to enhance agricultural employment. The use of renewable energy is expected to be a solution for the decrease in employment in the agricultural sector along with the GDP decrease. Governments can make sure that all funds allocated to an agroecological program or policy are used to increase agricultural renewable energy use. Renewable energy regulations should be assessed based on their ability to reduce pollution while also ensuring a reliable energy supply for the agricultural sector at an affordable price.

Keywords

• Renewable energy
• agricultural employment
• GDP
• ARDL

Endonezya'da Tarımın Yenilenebilir Enerji ve GSYİH Payının Artırılması: Tarım Sektörü İstihdamı Üzerindeki Etkilerin Analizi

Abstract

Endonezya'da yenilenebilir enerji kapasitesi, tarım sektörü GSYİH ve tarım istihdamı 1990'dan 2019'a düşüş eğilimi yaşamıştır. Bu eğilim Endonezya'nın yenilenebilir enerjiyi enerji kaynağı olarak kullanma hedefini, tarım sektöründeki istihdamı ve Endonezya ekonomisi için tarımın önemini olumsuz etkiliyor. Bu konulara dayanarak, bu araştırma, Autoregressive Distributed Lag (ARDL) modelini uygulayarak yenilenebilir enerji kullanımı ve tarım sektörü GSYİH'sının tarım sektörünün istihdam oranı üzerindeki etkisini incelemiştir. Ampirik bulgulara göre, toplam yenilenebilir enerji arzı, kısa ve uzun dönemde tarım sektörü istihdamını iyileştirmektedir; ancak tarım sektörü GSYİH'si istihdamı yalnızca kısa vadede artırır ve uzun vadede olumsuz etkiler. Yenilenebilir enerjinin tarımsal istihdamı arttırma potansiyeline sahip olduğunu öne sürüyor. Yenilenebilir enerji kullanımının GSYİH azalması ile birlikte tarım sektöründeki istihdam azalmasına çözüm olması beklenmektedir. Hükümetler, tarımsal yenilenebilir enerji kullanımını artırmak için bir agroekolojik program veya politikaya tahsis edilen tüm fonların kullanılmasını sağlayabilir. Yenilenebilir enerji politikası, tarım sektörü için uygun fiyata güvenilir enerji arzını sağlarken kirliliği azaltma yeteneklerine göre değerlendirilmelidir.

Keywords

• Yenilenebilir enerji
• tarımsal istihdam
• GSYİH
• ARDL

References

1. Adelaja, S., & Hailu, Y. G. (2008). Renewable Energy Development and Implications to. Director, 1–15. Retrieved from http://ageconsearch.umn.edu/bitstream/6132/2/470566.pdf
2. Arendonk, A. van. (2015). The development of the share of agriculture in GDP and employment: A case study of China, Indonesia, the Netherlands and the United States. Retrieved from https://edepot.wur.nl/342795
3. Deka, A., & Dube, S. (2021). Analyzing the causal relationship between exchange rate, renewable energy and inflation of Mexico (1990–2019) with ARDL bounds test approach. Renewable Energy Focus, 37(June), 78–83. https://doi.org/10.1016/j.ref.2021.04.001
4. Diao, X., Hazell, P., Resnick, D., & Thurlow, J. (2007). The role of agriculture in development: Implications for Sub-Saharan Africa. In Research Report of the International Food Policy Research Institute. https://doi.org/10.2499/9780896291614rr153
5. Dickey, D. A., & Fuller, W. A. (2012). Journal of the American Statistical Association Distribution of the Estimators for Autoregressive Time Series with a Unit Root Distribution of the Estimators for Autoregressive Time Series With a Unit Root. July 2015, 37–41. https://doi.org/10.1080/01621459.1979.10482531
6. Garrett-Peltier, H. (2017). Green versus brown: Comparing the employment impacts of energy efficiency, renewable energy, and fossil fuels using an input-output model. Economic Modelling, 61(November 2016), 439–447. https://doi.org/10.1016/j.econmod.2016.11.012
7. Ghosh, S. (2009). Electricity supply, employment and real GDP in India: evidence from cointegration and Granger-causality tests. Energy Policy, 37(8), 2926–2929. https://doi.org/10.1016/j.enpol.2009.03.022
8. Gollin, D., Parente, S., & Rogerson, R. (2002). The Role of Agriculture in Development. The New Palgrave Dictionary of Economics, 92(2), 142–149. https://doi.org/10.1057/9780230226203.0037

9. Hamit-Haggar, M. (2012). Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective. Energy Economics, 34(1), 358–364. https://doi.org/10.1016/j.eneco.2011.06.005
10. Hillebrand, B., Buttermann, H. G., Behringer, J. M., & Bleuel, M. (2006). The expansion of renewable energies and employment effects in Germany. Energy Policy, 34(18), 3484–3494. https://doi.org/10.1016/j.enpol.2005.06.017
11. Kuznets, S. (1961). Economic growth and the contribution of agriculture: notes on measurement (No. 979-2016-77077).
12. Lehr, U., Mönnig, A., Missaoui, R., Marrouki, S., & Salem, G. Ben. (2016). Employment from Renewable Energy and Energy Efficiency in Tunisia – New Insights, New Results. Energy Procedia, 93(March), 223–228. https://doi.org/10.1016/j.egypro.2016.07.174
13. Li, R., Xu, L., Hui, J., Cai, W., & Zhang, S. (2022). China’s investments in renewable energy through the belt and road initiative stimulated local economy and employment: A case study of Pakistan. Science of The Total Environment, 835(April), 155308. https://doi.org/10.1016/j.scitotenv.2022.155308
14. Ministry of Investment. (2021). Harnessing renewable energy investment sector in Indonesia. Retrieved from Ministry of Investment: https://bit.ly/3yo08RJ
15. Nagatomo, Y., Ozawa, A., Kudoh, Y., & Hondo, H. (2021). Impacts of employment in power generation on renewable-based energy systems in Japan— Analysis using an energy system model. Energy, 226, 120350. https://doi.org/10.1016/j.energy.2021.120350
16. Nasirov, S., Girard, A., Peña, C., Salazar, F., & Simon, F. (2021). Expansion of renewable energy in Chile: Analysis of the effects on employment. Energy, 226. https://doi.org/10.1016/j.energy.2021.120410
17. OECD. (2017). Employment Implications of Green Growth: Linking jobs, growth, and green policies. OECD Report for the G7 Environment Ministers. Oecd, (June), 1–24. Retrieved from https://www.oecd.org/environment/Employment-Implications-of-Green-Growth-OECD-Report-G7-Environment-Ministers.pdf
18. OECD. (2021, October 28). Renewable Energy. Retrieved December 2021, from OECD: https://data.oecd.org/energy/renewable-energy.htm
19. Ogbanga, A. (2018). Agricultural Development and Employment Generation in Nigeria. Allwell International Journal of Advanced Studies in Ecology, 5(1), 1–22. Retrieved from http://internationalpolicybrief.org/journals/international-scientific-research-consortium-journals/
20. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
21. Pestel, N. (2019). Employment effects of green energy policies. IZA World of Labor, (July 2014), 1–11. https://doi.org/10.15185/izawol.76.v2
22. Peter C. B. Phillips, & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335–346. https://doi.org/10.2307/2336182
23. Proença, S., & Fortes, P. (2020). The social face of renewables: Econometric analysis of the relationship between renewables and employment. Energy Reports, 6, 581–586. https://doi.org/10.1016/j.egyr.2019.09.029
24. Sari, A., & Akkaya, M. (2016). Contribution of Renewable Energy Potential to Sustainable Employment. Procedia - Social and Behavioral Sciences, 229, 316–325. https://doi.org/10.1016/j.sbspro.2016.07.142
25. Stavropoulos, S., & Burger, M. J. (2020). Modelling strategy and net employment effects of renewable energy and energy efficiency: A meta-regression. Energy Policy, 136(August 2019), 111047. https://doi.org/10.1016/j.enpol.2019.111047
26. The World Bank. (2021, January 29). Employment in agriculture (% of total employment) (modeled ILO estimate) - Turkey. Retrieved December 2021, from The World Bank: https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS?locations=TR
27. The World Bank. (2022, April 27). Agriculture, forestry, and fishing, value added (% of GDP). Retrieved from The World Bank: https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS
28. UNEP. (2014). United Nations Environment Assembly of the United Nations Environment Programme. In United Nations Environment Programme. Nairobi. Retrieved from https://papersmart.unon.org/resolution/uploads/k1900699.pdf
29. Zaman, S., Zaman, Q. uz, Zhang, L., Wang, Z., & Jehan, N. (2022). Interaction between agricultural production, female employment, renewable energy, and environmental quality: Policy directions in context of developing economies. Renewable Energy, 186, 288–298. https://doi.org/10.1016/j.renene.2021.12.131
30. Zhao, X., & Luo, D. (2017). Driving force of rising renewable energy in China: Environment, regulation and employment. Renewable and Sustainable Energy Reviews, 68(January 2016), 48–56. https://doi.org/10.1016/j.rser.2016.09.126