1. Introduction
Palm oil is an important and versatile vegetable oil and it is being widely used as raw materials for food and non-food uses (Gan & Li, 2014). Palm oil appears to be the most productive crop, with a production cost that is much lower than other vegetable oils. As highlighted by Lam, Tan, Lee, and Mohamed (2009), its production cost is US$228/t that is much lower than soybean oil and rapeseed oil, which is ranged between US$400/t and US$900/t. Palm oil has surpassed soybean oil to become the world primary vegetable oil. Table 1 displays the production of palm oil in relation to other major vegetable oils during the period from 1980 to 2019. Over the past four decades, the world palm oil production has increased rapidly from 4.90 million MT (1980) to 75.19 million MT (2019). In 2019, palm oil dominated world production, comprising 36.4% of the world total production, which is
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International Journal of Business and Economy (IJBEC) eISSN: 2682-8359 [Vol. 2 No. 2 June 2020]
Journal website: http://myjms.mohe.gov.my/index.php/ijbec
SELECTED REVIEW ON CLIMATE CHANGE AND PALM OIL PRICES
Yan-Ling Tan1*, Bee-Hoong Tay2, Roslina Mohamad Shafi3 and Nor Hamiza Mohd Ghani4
1 2 3 4 Faculty of Business and Management, Universiti Teknologi MARA Cawangan Johor Kampus Segamat,
MALAYSIA
*Corresponding author: [email protected] Article Information:
Article history:
Received date : 17 February 2020 Revised date : 18 February 2020 Accepted date : 30 May 2020 Published date : 3 June 2020 To cite this document:
Tan, Y., Tay, B., Mohamad Shafi, R., &
Mohd Ghani, N. (2020). SELECTED REVIEW ON CLIMATE CHANGE AND PALM OIL PRICES. International Journal Of Business And Economy, 2(2), 62-74.
Abstract: Compared to other vegetable oils, palm oil remains as one of the productive crops due to the higher yields and lower production costs. However, global climate change poses a fundamental risk to agriculture. The potential effects of climate change include prolonged periods of drought, wildfires, heavy floods, excessively high temperatures and changes in precipitation patterns. The increased events of extreme weather can influence plant growth and promote crop disease, subsequently affect the price movement. Therefore, there is an evident need to review the impacts of changing climate on palm oil prices. It is notable that previous studies have assumed the symmetric relation between palm oil prices and climatic conditions. Nevertheless, there remain comparable limited studies on the asymmetric response of palm oil prices to climate change. The presence of asymmetries will provide some new insights for related agencies by developing a more sustainable agriculture strategy in order to reduce the vulnerability of palm oil to uncertain weather conditions.
Keywords: Palm oil price, climate change, crop production.
Table 2 presents the ten largest palm oil producing countries and its share of total global palm oil production. Malaysia appeared as the largest producer between the years 1980 and 2000. However, Indonesia dominated the global palm oil production from 2000s onwards. As noted in Table 2, Indonesia is ranked as the largest producing country, accounted for approximately 57.2% of world production in 2019, Malaysia is second with 27.3%, and Thailand third with 4.0%, followed by Colombia and Nigeria with 2.2% and 1.4%, respectively. This shows that currently Indonesia and Malaysia are producing 84.4% out of the 75.2 million MT of global palm oil.
Table 1: Major vegetable oils, 1980-2019 (million MT)
Vegetable oils 1980 1990 2000 2010 2015 2016 2017 2018 2019
Oil, Coconut 2.85 2.93 3.58 3.59 3.31 3.39 3.66 3.67 3.58
Oil, Cottonseed 2.89 3.71 3.53 4.97 4.30 4.43 5.18 5.12 5.19
Oil, Olive 1.90 1.45 2.49 3.30 3.13 2.48 3.26 3.09 3.36
Oil, Palm 4.90 11.03 24.25 49.21 58.86 65.18 70.63 73.90 75.19 Oil, Palm Kernel 0.55 1.43 3.09 5.83 7.01 7.63 8.25 8.55 8.71
Oil, Peanut 2.33 3.29 4.72 5.23 5.42 5.70 5.90 5.86 6.00
Oil, Rapeseed 3.88 8.77 13.89 23.41 27.35 27.55 28.11 27.52 26.98 Oil, Soybean 12.58 15.77 26.82 41.48 51.56 53.81 55.15 55.85 56.86 Oil, Sunflowerseed 4.63 8.07 8.00 12.08 15.38 18.15 18.51 19.46 20.63 Total 36.51 56.45 90.37 149.08 176.29 188.33 198.64 203.02 206.50 Source : USDA (2019)
Table 2: Palm oil production, 1980-2019 (% of world total)
Country 1980 1990 2000 2010 2015 2016 2017 2018 2019 Indonesia 15.36 24.02 34.23 47.96 54.37 55.23 55.93 56.16 57.19 Malaysia 54.98 54.66 49.23 37.01 30.07 28.93 27.87 28.15 27.26 Thailand 0.39 1.81 2.39 3.72 3.07 3.84 3.94 3.92 3.99 Colombia 1.63 2.28 2.14 1.53 2.15 1.69 2.31 2.20 2.23 Nigeria 10.62 5.44 3.01 1.97 1.62 1.52 1.45 1.37 1.35 Guatemala 0.00 0.05 0.51 0.47 1.06 1.14 1.21 1.15 1.13 Ecuador 0.90 1.36 0.92 0.77 0.88 0.87 0.81 0.78 0.84 Honduras 0.37 0.58 0.61 0.65 0.83 0.95 0.82 0.78 0.77 Brazil 0.35 0.63 0.45 0.55 0.71 0.74 0.71 0.71 0.72 Cote d'Ivoire 3.00 2.52 1.02 0.73 0.71 0.75 0.68 0.70 0.68 Others 12.40 6.64 5.48 4.63 4.53 4.36 4.27 4.07 3.83 Source : USDA (2019)
In addition, Table 3 presents the world's ten largest palm oil exporting and importing countries.
Indonesia has overtaken Malaysia to become the world's largest exporting palm oil country according to the recent statistic from USDA (2019). Indonesia is still, by far, the leading exporter globally, exporting palm oil valued at a total of 30.3 million MT in 2019. The latest figures represent Indonesia's palm oil exports were equivalent to 56.5 percent of the world total palm oil exports in 2019, up from 44.0% in 2010. Malaysia maintained as the world's number two exporter of palm oil, with 2019 exports of 18.0 million MT or equivalent to 33.5% of total world exports market; while the remaining exporters recording relatively small in number, such as Guatemala, Colombia and Papua New Guinea with share of 1.5%, 1.4% and 1.0% of total world exports. This shows that Indonesia and Malaysia have consistently constituted for more than 90% of global exports of palm oil since the year 2000.
In 2019 over 52.1 million MT of palm oil were imported worldwide. India was the leading palm oil importer, with an annual import of 10 million MT, up from 5.6 million MT in 2010 (Panel b, Table 3). Moreover, the combined palm oil imports of India, European Union, China, Pakistan and Bangladesh total almost 29.7 million MT, accounted for over half of the world total imports in 2019.
Other notable palm oil importing countries came from United States, Egypt, Philippines, Kenya, and Burma with 3.0%, 2.4%, 2.3%, 1.8% and 1.7% of the total world imports, respectively in that year.
Table 3: Leading palm oil exporters and importers of palm oil, 1980-2019 (a) Palm oil exports (million MT)
Country 1980 1990 2000 2010 2015 2016 2017 2018 2019 Indonesia 0.21 1.46 4.78 16.42 22.91 27.63 26.97 29.20 30.30 Malaysia 2.43 5.43 10.58 17.15 16.67 16.31 16.47 18.36 18.00
Guatemala 0.00 0.00 0.04 0.21 0.66 0.72 0.80 0.83 0.81
Colombia 0.00 0.01 0.07 0.14 0.42 0.50 0.70 0.68 0.77
Papua New Guinea 0.04 0.15 0.31 0.58 0.55 0.58 0.65 0.66 0.52
Benin 0.00 0.01 0.02 0.26 0.45 0.50 0.48 0.46 0.47
Honduras 0.00 0.00 0.04 0.13 0.38 0.45 0.37 0.28 0.43
Thailand 0.00 0.00 0.20 0.38 0.04 0.31 0.35 0.28 0.38
Ecuador 0.00 0.00 0.00 0.25 0.31 0.29 0.29 0.20 0.30
Cote d'Ivoire 0.06 0.13 0.00 0.23 0.22 0.16 0.22 0.29 0.29
Others 0.61 1.17 0.50 1.58 1.23 1.34 1.34 1.37 1.42
Total 3.36 8.35 16.53 37.33 43.84 48.80 48.62 52.60 53.67
(b) Palm oil imports (million MT)
Country 1980 1990 2000 2010 2015 2016 2017 2018 2019
India 0.43 0.21 3.04 5.58 8.86 9.34 8.61 9.71 10.00
European Union 0.00 0.00 2.89 4.94 6.72 7.22 7.08 7.63 7.38
China 0.02 1.29 2.03 5.71 4.69 4.88 5.32 6.80 7.20
Pakistan 0.23 0.78 1.30 2.06 2.72 3.08 3.09 3.28 3.45
Bangladesh 0.08 0.08 0.33 1.00 1.51 1.35 1.64 1.65 1.70 United States 0.15 0.13 0.18 0.98 1.31 1.37 1.53 1.53 1.55
Egypt 0.00 0.37 0.50 1.28 1.04 1.32 1.10 1.02 1.25
Philippines 0.01 0.00 0.04 0.55 0.94 1.13 1.19 1.12 1.22
Kenya 0.10 0.20 0.36 0.50 0.70 0.77 0.76 0.91 0.92
Burma 0.03 0.15 0.21 0.39 0.79 0.81 0.85 0.95 0.91
Others 2.10 5.11 4.34 12.36 13.44 14.62 15.37 16.78 16.56 Total 3.14 8.31 15.19 35.35 42.70 45.87 46.52 51.37 52.14 Source : USDA (2019)
Moreover, Figure 1 plots the real vegetable oil prices from 1980 to 2019. It can be observed that coconut oil, palm oil, soybean oil, groundnut oil and palm kernel oil prices demonstrate similar patterns, showing the relatively close co-movement among the vegetable oil prices. Over the period, the groundnut oil price was considerably higher than other vegetable oil prices while the palm oil price was the lowest among the major vegetable oil prices.
Figure 1: Real vegetable oil prices (US$/MT) Source : The World Bank (2020)
0.00 500.00 1000.00 1500.00 2000.00 2500.00
Coconut oil Groundnut oil
Palm oil Palm kernel oil
Soybean oil
2. Literature Review
Theoretically, market price is determined by the interaction between consumers and suppliers. In other words, changes in demand and supply help to determine the market price movement (Borychowski & Czyżewski, 2015; Owen, Chowdhury, & Garrido, 1997; Wong, Lee, & Wong, 2019). The demand side differences are viewed as arising from the physical and chemical properties required by end-uses and consumers’ preferences as well as incomes, and hence, prices; while the supply-side differences are driven by production costs including climatic conditions (Owen et al., 1997). Other demand and supply side factors also contribute to the price changes. For instance, population growth, economic development, monetary policy, exchange rate, speculation and production of liquid biofuels are the determinants from the demand side. The supply side factors are supply shocks (weather conditions), stocks, crops production growth, trade policy and crude oil prices (Borychowski & Czyżewski, 2015).
2.1 Vegetable Oil Substitution
Substitution of vegetable oil prices also received a great deal of support from previous studies (Griffith & Meilke, 1979; Hameed & Arshad, 2009; In & Inder, 1997; Owen et al., 1997; Ubilava &
Holt, 2013). Using vector autoregression (VAR) model, In and Inder (1997) and Owen et al. (1997) reached different conclusions. The former suggested that the existence of long-run relationship among the edible oil prices over the period 1986-1990, while the latter found no evidence of cointegration between the vegetable and tropical oil prices from 1971 to 1993. On the other hand, Hameed and Arshad (2009) examined the link between crude oil price and selected vegetable oil prices using Engle-Granger two-stage estimation procedure from 1983 to 2008. Only unidirectional causality is identified, running from petroleum price to palm oil, rapeseed oil, soybean oil and sunflower oil prices.
2.2 Demand for Palm Oil
The palm oil studies has received a great deal of attention in the literature (Ab Rahman, Abdullah, Balu, & Shariff, 2013; Ab Rahman, Abdullah, & Shariff, 2012; Awad, Arshad, Shamsudin, & Yusof, 2007;Hassan & Balu, 2016; Hassan, Ahmad, & Balu, 2018; Kamil & Omar, 2016; Rifin, 2010;
Zainal, Shamsudin, Mohamed, & Adam, 2012; Zakaria, Salleh, & Balu, 2017b; Zakaria, Balu, Baharim, & Rapiee, 2018; Zakaria, Salleh, & Balu, 2017a). Specifically, the matters relating to palm oil demand remain significant today. Awad et al. (2007) have made a step forward on import demand for palm oil in ten Middle East and North African (MENA) countries using the autoregressive distributed lag (ARDL) approach. Their findings suggested that the palm oil prices and income play a significant role in promoting palm oil demand. Price of substitute oils namely soybean oil, corn oil, rapeseed oil and sunflower seed oil appear to be the significant factor in palm oil demand in these countries. Apart from this, other explanatory variables such as palm oil discount, global petroleum prices boom, anti-palm oil campaign, trade embargos and exchange are discovered to be highly significant in affecting the demand.
More recently, several studies have used the similar technique in analyzing the palm oil demand between 1980 and 2015 in India (Zakaria et al., 2017a), Turkey (Zakaria et al., 2018), and China (Zakaria et al., 2017b). Similar findings have also reported by Zakaria et al. (2018) where a lower palm oil price, a higher income and sunflower oil price result in a higher demand for palm oil in Turkey. Consistent with the demand theory, the empirical findings by Zakaria et al. (2017b) suggested that income and the difference between soybean oil and palm oil prices affect demand for palm oil positively while soybean meal price shows an inverse effect on palm oil demand in China.
On the contrary, Zakaria et al. (2017a) discovered different findings, where income and price discount of palm oil over soybean oil have a significant negative impact on demand in India in the long-run, suggesting that palm oil is viewed as an inferior good in India. Additional variable of population is also found to have a positive and significant effect on demand for palm oil.
Other than the countries discussed above, the focus has been shifted to Indonesia and Malaysia. Rifin (2010) studied Indonesia's palm oil position in the international market using the error correction model (ECM) technique. The findings implied that a rise in global income will likely boost the world palm oil demand. Apart from that, Rifin also concluded that palm oil products from Indonesia and Malaysia complementing one another, thereby future collaboration between Indonesia and its neighbouring country are needed in order to increase palm oil demand globally. On the other hand, Hassan and Balu (2016) assessed the long-run and short-run links between palm oil price, soybean oil price, oil palm production and total export in Malaysia from 1999 to 2015 by employing the vector error correction model (VECM) approach. Interestingly, they recorded that in the long-run, palm oil price is not necessarily influenced by soybean oil price; while other determinants such as palm oil supply and extreme weather events may affect the palm oil price. Recently, applying different econometric techniques namely autoregressive distributed lag (ARDL), integrated moving average (ARIMA) and autoregressive integrated moving average with exogenous inputs (ARIMAX), Khalid, Hamidi, Thinagar and Marwan (2018) suggested that stock of palm oil stock, crude petroleum oil price and soybean oil price are found to statistically affect the spot price of palm oil from 2008 to 2017 in Malaysia. They also noted that ARIMAX model appears to be more satisfactory in forecasting the palm oil price.
2.3 Climate Change and Crop Production
The topic of climatic condition is often linked to various economic variables have received wide attention over the last few decades. Climate change will raise the risk of some types of extreme weather events such as excessive heat or lack of water, floods, droughts, changes in rainfall patterns and higher frequency and severity of extreme weather events (Bandara & Cai, 2014; Wheeler & von Braun, 2013). These events will subsequently reduce the food production by interrupting the crops growth, encouraging crop disease and increasing sensitivity of crops to insect pests (CCSP, 2008).
Consequently, the loss of yield affects food production, food prices and lastly threaten food security (Bandara & Cai, 2014). Bandara and Cai (2014) further claimed that among the regions, South Asia was affected the most by global climate change. Specifically, climate change is found to have adverse effects on food production in Bangladesh, India, Nepal, Pakistan and Sri Lanka and then raises the market food price.
Several studies have focused on the impact of climate change on global crop production (Haile, Wossen, Tesfaye, & von Braun, 2017; Lesk, Rowhani, & Ramankutty, 2016; Rosenzweig & Parry, 1994). According to Rosenzweig and Parry (1994), there exists differences in agricultural responses to climate change between developed and developing nations. In addition to this, developing countries have greater likelihood to bear more effects of the climate change.
On the contrary, Lesk et al. (2016) offered a different finding that developed countries are the most affected nations by extreme weather disasters. Another interesting finding from Lesk et al. (2016) is national cereal production has declined by about 9% to 10% due to droughts and extreme heat between the years 1964 and 2007 whereas no effect was identified on agriculture as a result of floods and extreme cold. According to Haile, Wossen, Tesfaye, and von Braun (2017), climate changes are likely to reduce global crop production such as maize, wheat, rice and soybeans by roughly 9% and 23 % in the 2030s and 2050s, respectively. It is predicted that climate change would reduce maize (22%), sorghum (17%), millet (17%), groundnut (18%), and cassava (8%) yields by 2050 in Sub- Saharan Africa (SSA) (Schlenker & Lobell, 2010).
2.4 Climate Change and Other Commodity Prices: Nonlinear Effects
In recent years, there has been an abundant body of literature have endeavoured in studying the El Niño Southern Oscillation (ENSO) phenomena and commodity prices (Brunner, 2002; Keppenne, 1995; Letson & McCullough, 2001; Peri, 2017; Ubilava, 2012; Ubilava, 2017a, 2017b;Ubilava &
Holt, 2013). Keppenne (1995) examined the ENSO-soybean future prices linkage between 1972 and 1993, showing that soybean future prices are highly responsive to the ENSO events. While similar study such as Letson and McCullough (2001) found no relationship exists between ENSO events and soybean spot prices in U.S. over the period 1950-2000. Within the context of VAR model by Brunner (2002), ENSO is found to have a significant effect on real commodity prices in G7 countries from 1963 to 1998. This indicates that the commodity price inflation rises by about 3.5 to 4 percentage points as a result of one-standard-deviation positive shocks in ENSO. Among these commodities, coconut oil is more impacted by ENSO events, followed other oils such as palm, soybean and groundnut and other food crops namely rice, wheat, soybeans, and maize.
However, the recent studies have diverted from the linear impacts to nonlinear impacts of ENSO on wheat prices (Ubilava, 2017a), 43 commodity prices (Ubilava, 2017b), vegetable oil prices (Ubilava
& Holt, 2013) and coffee prices (Ubilava, 2012) using vector smooth transition autoregressive (VSTAR) or smooth transition vector error correction (STVEC) modelling framework. This is because the effects of ENSO anomalies to primary commodity price differ across the export regions (Ubilava, 2017a) and production geography (Ubilava, 2012). For instance, Ubilava (2017a) reported that wheat prices tend to decrease after El Nino events, and increase after La Nina events. This implies that a positive ENSO shock (El Nino) reduces wheat prices, while a negative ENSO shock (La Nina) increases wheat prices in five major wheat exporting regions namely United States, the European Union, Australia, Canada, and Argentina from 1982 to 2014. Nevertheless, Ubilava and Holt (2013) evidenced that positive deviations during El Nino events can result in a rise in vegetable oil prices; whereas negative deviations during La Nina events can result in a decrease in major vegetable oil prices spanning between the years 1972 and 2010.
In addition to the ENSO-price nexus studies, there is literature focused on nonlinearities using alternative indicator of climate change such as carbon dioxide (CO2) and its squared along with error correction model (ECM) (Wong et al., 2019) and alternative nonlinear framework namely nonlinear autoregressive distributed lag (NARDL) (Nsabimana & Habimana, 2017). Wong et al. (2019) supported the existence of non-linear U-shaped between CO2 and food price in Malaysia over the period 2010-2017. Specifically, raising the CO2 by 1% leads to a 1.9% fall in the food price.
However, the food price will increase as the CO2 continues to increase more than the threshold level.
While Nsabimana and Habimana (2017) evidenced the asymmetric relations that the responses of food crop prices to rainfall shocks are different in both the short-run and long-run in Rwanda. These food crop prices include beans, potatoes and cassava roots
2.5 Climate Change and Palm Oil in Malaysia
As such, there is a pressing need for understanding the changing climate on palm oil production and its prices (Ab Rahman et al., 2013; Hassan et al., 2018; Kamil & Omar, 2016; Zainal et al., 2012).
In the case of world's leading palm oil producing countries like Indonesia and Malaysia, palm oil production is highly sensitive to extreme weather phenomena (Ab Rahman et al., 2013) and thereby affect the palm oil prices (Ubilava & Holt, 2013). On average, annual temperature between 28°C and 31°C are more favourable conditions for fruit growing and farming (MOSTE, 2000);
nevertheless, nearly 12% of the current oil palm areas are not suitable for palm oil production if the average temperatures keep rising will in turn contribute to drought. Sufficient rainfall on the one hand is beneficial for oil palm productivity except heavy rainfall will cause severe flood scenarios (Fleiss, Hill, McClean, & Lucey, 2017; MOSTE, 2000). Rainfall shortage, on the other hand will increase the risk of yield losses (Fleiss et al., 2017).
Zainal et al. (2012) explored the responsiveness of palm oil production to climate change on three main regions namely Peninsular Malaysia, Sabah and Sarawak covering the years 1980-2010. By formulating the net revenue as dependent variable while temperature and its square as well as rainfall and its square as explanatory variables, they claimed that there exists a nonlinear relation between climate change (temperature and rainfall) and net revenue. This indicates that high temperature and a rise in rainfall will affect the production of palm oil inversely which in turn reduces the net revenue from palm oil production. For instance, the findings suggested that net revenue is expected to decrease in Peninsular, Sabah and Sarawak by about RM 44.52, RM 45.60 and RM 37.70, respectively associated with a 1°C rise in temperature.
More recently, empirical studies have been shifted to the ENSO conditions and palm oil (Ab Rahman et al., 2013, 2012; Hassan et al., 2018; Kamil & Omar, 2016). The strong occurrences of ENSO will influence the supply of palm oil especially from palm oil producing countries and, consequently drives the price movements (Ab Rahman et al., 2013; Kamil & Omar, 2016). El Niño events are connected to a warming of the ocean surface in the central and eastern tropical Pacific Ocean. While La Niña is an opposite phenomenon of the El Niño, will lead to persistent cooling of these same areas (Hassan et al., 2018). El Niño will bring heat and drought, causing in rainfall reduction and leads to larger reductions in palm oil yield. More specifically, the pervasive hot and dry weather effects appear to have time-lag effects on palm oil, as such the impacts of El Niño will take roughly one to two years after the event (Ab Rahman et al., 2013). Besides that, La Niña causes increased rainfall and lead to significant flooding in planted areas and harvest disruptions (Ab Rahman et al., 2013, 2012).
Accordingly, Ab Rahman et al. (2013) assessed the impact of El Niño and La Niña on palm oil price in Malaysia from 1990 to 2012. Their regression results suggested that La Niña and El Niño events affect the crude palm oil prices positively while influence crude palm oil productions negatively.
Crude palm oil productions are expected to fall by about 0.09% (during the La Niña events) and 0.02% (during El Niño events); whereas crude palm oil prices are expected to rise during the La Niña and El Niño events by about 0.03% and 0.02%, respectively. Unlike Ab Rahman et al. (2013), Ab Rahman et al. (2012) assessed the economic impacts of the Northeast monsoon and La Niña on palm oil production in Malaysia using monthly data (1990 - 2010) and annual data (1975-2011).
They concluded that palm oil production is responsive to the La Niña phenomenon, indicating that palm oil production is significantly lower during the La Niña in most cases. The palm oil production losses from La Niña events are estimated to be 3.5% (2010) and 2.2% (2011) as compared to without La Niña.
Kamil and Omar (2016) also studied the ENSO events on palm oil industry in Malaysia from 2006 to 2015. They claimed that the responses of palm oil are subjected to the intensity of ENSO phenomena. For instance, a moderate or strong El Niño tend to have larger effect on palm oil pattern in relative to a mild and weak El Niño. In their regression, they only considered the impacts of rainfall on palm oil price along with other regressors namely soybean oil price, palm oil production and palm oil export. Interestingly, the regression results revealed that all the regressors exhibit significant influence on palm oil price except rainfall. Lastly, using more advanced econometric method of vector error cointegration model (VECM), Hassan et al. (2018) found that fresh fruit bunch yield and Ocean Niño Index (ONI) affect the palm oil production in Malaysia from 2007 to 2016. In addition, the error correction term (ECT) showed the disequilibrium among the studied variables will be corrected at the speed of 23.3% each month, or approximately four months will be taken for palm oil market to have full adjustment.
3. Conclusion and Recommendations
Drawing together previous studies as displayed in Table 4, it can be highlighted that, majority literature would primarily favour the interchangeability and substitution of major vegetable oils (Griffith & Meilke, 1979; Hameed & Arshad, 2009; Hassan & Balu, 2016; In & Inder, 1997; Owen et al., 1997; Ubilava & Holt, 2013) and specifically focused on demand for palm oil (Awad, Arshad, Shamsudin, & Yusof, 2007; Zakaria, Salleh, & Balu, 2017b; Zakaria, Balu, Baharim, & Rapiee, 2018; Zakaria, Salleh, & Balu, 2017a) within the context of cointegration framework. There exists a wide range of different estimating techniques used to examine the long-run linkage between the vegetable oils prices. Common econometric techniques include Granger causality test, VAR, ECM, VECM, ARDL, ARIMA and ARIMAX.
On top of this, some existing studies frequently concentrated on the ENSO-primary commodity prices linkage based on linear framework such as VAR (Brunner, 2002) and extended to relatively advance econometric techniques within nonlinear framework of vector smooth transition autoregressive (VSTAR) or smooth transition vector error correction (STVEC) (Ubilava, 2012;
Ubilava, 2017a, 2017b; Ubilava & Holt, 2013). For the case of Malaysia, previous studies have maintained the assumption of a symmetric relation between palm oil prices and climate change (Ab Rahman et al., 2013, 2012; Hassan et al., 2018; Kamil & Omar, 2016). The symmetric relation implies that the effect of climate change on palm oil price is similar irrespective of a rise or a reduction in palm oil price. According to Wong et al. (2019), the discussion may turn out to be
Although various econometric techniques were used in previous literature, the results consistently suggest that primary commodity prices are vulnerable to ENSO events. Nevertheless, the indicators of climate change are certainly not limited to ENSO anomalies, special attention should be given to other climate change components include temperature, rainfall, extreme events, CO2 and ocean dynamic (Bellard, Bertelsmeier, Leadley, Thuiller, & Courchamp, 2012). In this regard, the influence of climatic conditions on palm oil prices should be extended to asymmetric cointegration framework of NARDL. Therefore, this study recommends that future studies on climate change- palm oil price nexus might consider alternative climate change indicators based on asymmetric cointegration framework. The evidence of asymmetries will shed some light on the responsible parties to plan and establishing policies that will sustain the production of palm oils regardless of the climatic conditions.
Table 4: Summary of the Literature Review
Author (Year) Period Method of
estimation Commodity
Climate change indicator Wong et al. (2019) 2010-2017 EG and ECM Food production index CO2 Khalid et al. (2018) 2008-2017 ARDL, ARIMA,
ARIMAX Palm oil -
Hassan et al. (2018) 2007-2016 VECM Palm oil ONI
Zakaria et al. (2018) 1980-2015 ARDL Palm oil -
Nsabimana and Habimana
(2017) 2000-2012 NARDL Food crop Rainfall
Peri (2017) 1960-2014 Multivariate GARCH Maize, soybean ENSO
Ubilava (2017a) 1982-2014 VSTAR Wheat ENSO
Ubilava (2017b) 1980-2016 Time-varying STAR 43 primary
commodities ENSO
Zakaria et al. (2017a) 1980-2015 ARDL Palm oil -
Zakaria et al. (2017b) 1980-2015 ARDL Palm oil -
Hassan and Balu (2016) 1999-2015 VECM Palm oil -
Kamil and Omar (2016) 2006-2015 OLS Palm oil Rainfall
Ab Rahman et al. (2013) 1990-2012 OLS Palm oil La Niña and
El Niño Ubilava and Holt (2013) 1972-2010 STVEC Vegetable oil ENSO Tack and Ubilava 2013 1950-2005 Pooled OLS Corn yields ENSO Ab Rahman et al. (2012)
1990-2010 and 1975-
2011
Survey and OLS Palm oil La Niña
Ubilava (2012) 1989-2010 STVEC Coffee ENSO
Zainal et al. (2012) 1980-2010 OLS Palm oil
Temperature and its square, rainfall and its square
Murad et al. (2010) 1990-2004 OLS Agriculture CO2
Rifin (2010) ECM Palm oil -
Hameed and Arshad (2009) 1983-2008 EG Vegetable oil -
Awad et al. (2007) ARDL Palm oil -
Brunner (2002) 1963-1998 VAR Non-oil primary
commodity ENSO
Letson and McCullough
(2001) 1950-2000 Granger causality Soybean ENSO
In and Inder (1997) 1986-1990 VAR Vegetable oil -
Owen et al. (1997) 1971-1993 VAR Vegetable and tropical
oils -
Griffith and Meilke (1979) 1959-1975
Coherence estimates, gain estimates and phase estimates
Fats and oils -
Notes: ARDL - Autoregressive distributed lag, ARIMA - Autoregressive integrated moving average, ARIMAX- Autoregressive Integrated Moving Average with exogenous inputs, CO2 - Carbon dioxide, ECM - Error correction model, EG - Engle-Granger, ENSO - El Niño / Southern Oscillation, GARCH - Generalised autoregressive conditional heteroskedasticity, NARDL - Nonlinear autoregressive distributed lag, ONI - Ocean Niño Index, OLS - Ordinary least squares, STAR - Smooth transition autoregressive, STVEC - Smooth transition vector error correction, VAR - Vector autoregression, VECM - Vector error correction model, VSTAR - Vector smooth transition autoregressive.
4. Acknowledgement
The authors gratefully acknowledge financial support from the Universiti Teknologi MARA Cawangan Johor Kampus Segamat for the Bestari Grant Phase 1/2019 - 600-UiTMCJ (PJIA.5/2).
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