Commodity Prices Research Articles

Global trade dynamics' impact on food pricing and supply chain resilience: A quantitative model

This study explores the influence of global trade dynamics on food pricing and supply chain resilience, focusing on the development of a quantitative model that examines the relationships between trade policies, economic indicators, and trade volumes. As fluctuations in trade policies—such as tariffs, quotas, and subsidies—directly impact food prices and market stability, this model provides a structured framework for assessing these variables’ interdependencies. By integrating data on trade flows, currency exchange rates, and commodity prices, the model quantifies the effects of protective and liberalized trade measures on both local and international food markets. This approach allows for identifying trends in pricing volatility linked to policy shifts, thereby offering insights into areas vulnerable to trade disruptions and external economic shocks. The resilience of the food supply chain is measured by evaluating metrics such as delivery times, supplier diversity, and inventory levels across regions affected by trade policy changes. The findings emphasize that trade barriers can result in price spikes, delays, and supply shortages, while open trade policies generally support market flexibility and pricing stability. Additionally, the model incorporates economic indicators such as Gross Domestic Product (GDP), inflation, and unemployment rates, which correlate with food demand and purchasing power, thereby further influencing price and supply chain stability. Future research could leverage this model to develop adaptive strategies that strengthen supply chain resilience under various global trade conditions. Suggested adaptations include diversifying suppliers, increasing inventory buffers, and enhancing predictive analytics for better demand forecasting. By integrating these adaptive measures, stakeholders can mitigate adverse effects on food accessibility and affordability in both developed and emerging economies. This research contributes to the literature on food security, highlighting the need for resilient trade policies and supply chain strategies in an increasingly interconnected global economy.

Volatility of Agricultural Commodities in Mubi and Dawanau Markets in Adamawa and Kano States in Nigeria

In this paper, we examined the volatility of agricultural commodities like Maize (White), Cowpeas (Brown) and Sorghum (White) in Adamawa and Kano state markets from 2015 – 2022 using data obtained from World Food Programme (WFD) food prices nga. We also compared the volatilities of these agricultural commodities in the Adamawa (Mubi) market with the volatilities of the same agricultural commodities in the Kano (Dawanau) market to study the evolution of the price of these agricultural commodities in the Adamawa (Mubi) and Kano (Dawanau) markets.

Robust Estimation for Threshold Autoregressive Moving-Average Models

Threshold autoregressive moving-average (TARMA) models extend the popular TAR model and are among the few parametric time series specifications to include a moving average in a nonlinear setting. The state dependent reactions to shocks is particularly appealing in Economics and Finance. However, no theory is currently available when the data present heavy tails or anomalous observations. Here we provide the first theoretical framework for robust M-estimation for TARMA models and study its practical relevance. Under mild conditions, we show that the robust estimator for the threshold parameter is super-consistent, while the estimators for autoregressive and moving-average parameters are strongly consistent and asymptotically normal. The Monte Carlo study shows that the M-estimator is superior, in terms of both bias and variance, to the least squares estimator, which can be heavily affected by outliers. The findings suggest that robust M-estimation should be generally preferred to the least squares method. We apply our methodology to a set of commodity price time series; the robust TARMA fit presents smaller standard errors and superior forecasting accuracy. The results support the hypothesis of a two-regime non-linearity characterized by slow expansions and fast contractions.

The effect of CHINA RAILWAY Express on alleviating the Red Sea Waterway Crisis: A GTAP model-based study

In November 2023, the Red Sea Waterway Crisis broke out, which caused the goods originally shipped to Europe via the Red Sea-Suez Canal Route to be transported around the Cape of Good Hope in Africa, thus increasing the shipping cost and shipping time. Firstly, this paper uses the Supply Chain Network Model and the Optimization Model to theoretically analyze the impact of this incident. Secondly, the Global Trade Analysis Project (GTAP) Model is employed to analyze the impact of the crisis on China. At the same time, it also explains whether the impact of the incident can be alleviated by using CHINA RAILWAY Express (CR Express). Finally, this paper compares the upward pressure changes of domestic commodity prices in China, Germany, France, Netherlands and Italy before and after the adoption of CR Express. The results of the study are as follows. (1) The outbreak of the Red Sea Waterway Crisis has a great negative impact on China's macro economy. When China switches to the use of CR Express, the impact on China's GDP and terms of trade is reduced. (2) The outbreak of the Red Sea Waterway Crisis has a greater impact on China's import trade than on its export trade. (3) The outbreak of the Red Sea Waterway Crisis has a great negative impact on China's textile and clothing industry and electronic equipment industry. (4) CR Express can ease the upward pressure of domestic commodity prices. Taken together, the above results can provide reference for China and other countries to cope with similar impact events.

Impact of COVID-19 trade suspension on price discovery in FPO-traded agri-derivatives

Purpose This study aims to examine whether agricultural commodities exhibited deviations in the lead-lag relationship between future and spot prices of farmer producer organizations (FPOs) traded commodities in the Indian derivative market after trade suspensions during Covid-19. The study may help buyers and sellers to get a fair price for their commodities after lockdown-trade disruptions. Design/methodology/approach The study applied the Granger causality (GC) test and the vector error correction model (VECM) to analyse short-run and long-run lead-lag relationships. Moreover, the study examined the pre-post-trade suspension effect on the lead-lag relationship of commodity prices. Findings The GC test results show that five out of the 13 agri-commodities have changed their lead-lag relationship from future to spot in the short run. Simultaneously, VECM captured changes in the lead-lag relationship for the same five commodities in the long run due to trade suspensions. Practical implications The findings indicate a reverse lead-lag relationship between future and spot prices for aforesaid commodities after trade suspension. The stakeholders may use the lead prices for these commodities to perform a fair trade. The study may be helpful in structuring price discovery strategy to achieve optimal price and efficient derivative trading. Originality/value To the best of the authors’ knowledge, this is the first study examining the effects of trade suspension on price discovery in FPO-traded agri-derivatives caused by the COVID-19 pandemic.

Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network

Big data is essential in the age of 4.0 industry as it becomes the basis of decision making. Deep learning research in the last few years has been proven effective in understanding complex big data patterns, especially in the finance sector. The rapid growth of the Indonesian stock market in the last 20 years, which was driven by globalization, prompted fluctuation in the Bursa Efek Jakarta (JKSE) which was influenced by stock prices, commodity prices, and exchange rate. This study identifies the main indicators of Indonesian stock market crisis, applies and compares deep learning models, particularly Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), in predicting stock prices. This study identified 20 JKSE crisis points between the 2002-2023 period with average return value at around -6%. All variables correlated positively with JKSE, with SET.BK as the highest correlated variable in lag 0. The American and European stock market, commodity price, and exchange rate tend to show a pattern opposite to the JKSE crisis. Predictor variables such as STI, HIS, KLSE, KS11, SET.BK, PSEI.PS, RUT, and USDIDR are chosen based on significant cross correlation and average return plot. Hyperparameter tuning and cross validation within a 3 years window concluded that the GRU model is accurate and efficient, with RMSE value at 43.35568 and MAE value at 33.66909 in the validation data.

The Effect of Food Price Stabilization on Inflation in West Nusa Tenggara 2019-2023

One of the primary contributors to the inflation rate in West Nusa Tenggara Province is the fluctuation of prices for food commodities. The objective of this study is to examine the extent to which fluctuations in food commodity prices, including those of rice, red chili, and shallots, contribute to inflationary trends in West Nusa Tenggara. The data used is monthly data on inflation and food commodity prices from 2019-2023 obtained from BPS (Central Bureau of Statistics), DKP (Food Security Agency), and the Agriculture Office of West Nusa Tenggara Province. The analysis method used is vector autoregression (VAR), which includes stationarity test, VAR stability test, optimal lag test, cointegration test, VAR estimation, impulse response function (IRF), and variance decomposition. The results of this study indicate that in the short term premium rice prices, curly red chili prices, and shallots do not affect inflation. Conversely, in the long run, the variable price of shallots does not affect inflation, while the variable price of premium rice and curly red chili prices exert an influence on inflationary pressures in West Nusa Tenggara Province. The Variance Decomposition Test indicates that the price of curly red chili contributes 37.24 percent to inflation in the fifth period, while premium rice has an effect of 8.66 percent on inflation in the second period and shallots have an effect of 7.93 percent on inflation in the fifth period.

Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Geopolitical Risk and Food Price Inflation: A GVAR Analysis of BRICS and the G7

ABSTRACT We investigate the influence of geopolitical risk on domestic food prices in 12 economies (BRICS and the G7) using a global vector autoregressive (GVAR) approach from 2003M1 to 2022M9. The results show that geopolitical risk shocks cause increases in international prices (oil, agricultural, and general commodity prices). We found that these shocks also provoke fluctuations in domestic food prices. The variance decomposition indicated that international prices and geopolitical risk contribute to the formation of domestic food prices. Finally, the estimates suggest relevant differences between the formation of domestic food prices in BRICS and G7 countries.

Incorporating a machine learning approach for commodity price prediction to generate a cut-off grade optimisation policy under grade uncertainty

ABSTRACT The economic importance of cut-off grade (COG) is that it differentiates ore from waste. Therefore, when COG is optimised, the economic value of a mining operation can be maximised. COG is optimised by considering several factors that include economic, geological, and operational parameters. Since uncertainty is inherent in some of these parameters, this paper used grade–tonnage distributions to incorporate grade uncertainty and a machine learning approach for commodity price prediction to account for price volatility when determining COGs. The paper demonstrates how uncertainty in these parameters can be incorporated in developing a dynamic COG policy over the life of mine.

Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy

Crude oil price forecasting has been one of the research hotspots in the field of energy economics, which plays a crucial role in energy supply and economic development. However, numerous influencing factors bring serious challenges to crude oil price forecasting, and existing research has room for further improvement in terms of an integrated research roadmap that combines impact factor analysis with predictive modelling. This study aims to examine the impact of financial market factors on the crude oil market and to propose a nonlinear combined forecasting framework based on common variables. Four types of daily exogenous financial market variables are introduced: commodity prices, exchange rates, stock market indices, and macroeconomic indicators for ten indicators. First, various variable selection methods generate different variable subsets, providing more diversity and reliability. Next, common variables in the subset of variables are selected as key features for subsequent models. Then, four models predict crude oil prices using common features as inputs and obtain the prediction results for each model. Finally, the nonlinear mechanism of the deep learning technology is introduced to combine above single prediction results. Experimental results reveal that commodity and foreign exchange factors in financial markets are critical determinants of crude oil market volatility over the long term, as observed in experiments conducted on the West Texas Intermediate and Brent oil price datasets. The proposed model demonstrates strong performance regarding average absolute percentage error, recorded at 2.9962% and 2.4314%, respectively, indicating high forecasting accuracy and robustness. This forecasting framework offers an effective methodology for predicting crude oil prices and enhances understanding the crude oil market.

Factors Affecting Export Demand for Malaysian Palm-Based Finished Products

The encouraging growth in volume and exports of palm-based finished products (PBFP) was directly related to an increase in their demand. It is thus important to investigate the factors influencing such demand. Our study attempts to elucidate this for the Malaysian PBFP in 10 major foreign markets, namely, Turkey, China, Iraq, Iran, Singapore, Romania, Australia, Pakistan, Russia, and Saudi Arabia. This study constitutes a panel data analysis for the 10 countries using data from 2007 to 2021 using the fully modified ordinary least squares (FMOLS) method. Pedroni and Westerlund cointegration test indicates the presence of a long-term relationship between the variables under study. The empirical results show that gross domestic product (GDP) has a long-term positive influence on the demand for PBFP, with every 1% increase in GDP will increase the demand for palm oil by 0.9%. In addition, population size (POP) also shows a positive sign and is statistically significant at the 1% level, indicating that a 1% increase in POP will lead to a 0.3% increase in PBFP demand. Substitute oil price also has a long-run positive influence where for every 1% increase in the price of both commodities will increase the demand for PBFP by 0.1%. Meanwhile the price of PBFP has a long-term positive influence on its demand. Thus, information regarding the factors influencing the demand for PBFP from our findings may help industry player and policymakers in formulating strategies to improve the markets, and academicians may also benefit from sharing the knowledge gained from the study.

A comprehensive review of treasury management practices and their impact on corporate sustainability in emerging economies: Strategic insights from the oil and gas sector

Treasury management practices are critical for ensuring financial stability and fostering corporate sustainability, particularly in the volatile oil and gas sector of emerging economies. This comprehensive review examines the relationship between treasury management practices and corporate sustainability, focusing on strategic insights that can be drawn from the oil and gas industry. The review highlights the importance of effective cash management, risk assessment, and liquidity optimization as key components of successful treasury operations. The study categorizes treasury management practices into several essential areas, including cash flow forecasting, working capital management, investment strategies, and risk management frameworks. Each category is analyzed concerning its contribution to corporate sustainability, emphasizing the need for a holistic approach that aligns financial objectives with environmental and social considerations. The findings indicate that robust treasury management practices can significantly enhance operational resilience, improve resource allocation, and support long-term sustainability initiatives. Furthermore, the review discusses the challenges faced by oil and gas companies in emerging economies, such as economic instability, regulatory uncertainty, and fluctuating commodity prices. These challenges necessitate the adoption of innovative treasury strategies that not only safeguard financial assets but also promote sustainable business practices. By integrating sustainability metrics into treasury decision-making, companies can better manage risks associated with environmental regulations and stakeholder expectations. Additionally, the role of technology in modernizing treasury management is examined, particularly in terms of digital tools and platforms that enhance data analysis and decision-making capabilities. The review underscores the potential for fintech solutions to streamline treasury operations and improve transparency, thereby contributing to enhanced corporate sustainability. In conclusion, this review provides strategic insights into the interplay between treasury management practices and corporate sustainability in the oil and gas sector of emerging economies. The insights presented herein are intended to guide industry practitioners in developing effective treasury strategies that foster sustainable growth while addressing the unique challenges of the emerging market context.

Currency power as a medium: Revisiting international cost transfer mechanisms in the U.S. subprime crisis

This paper endeavors to meticulously examine the disparities in developmental trajectories between industrialized and emerging economies, employing the nuanced concept of cost transfer within the theoretical framework of the "core-periphery" paradigm. We propose an intricate conceptual model to delve into the mechanisms through which economic crises are systematically shifted from the developed core to the peripheral developing regions, with particular emphasis on the pivotal roles of monetary hegemony and geopolitical dominance. Utilizing the U.S. subprime mortgage crisis as a seminal case study, we scrutinize the intricate dynamics of cost transfer, elucidating how the developed nations leverage their monetary supremacy and geopolitical prowess, often bolstered by military might and neo-liberal ideologies, to facilitate the offloading of inflationary pressures and commodity price hikes onto developing nations, notably China. Our findings contribute to the ongoing discourse on the entrenched inequalities between the Global North and South, emphasizing the asymmetrical power relations that underpin the process of cost transfer. Furthermore, this study enhances the research framework of cost-transfer theory and offers insights into the development of inequalities between developed and developing countries, aiding in the formulation of response strategies for developing countries facing cost transfer and mitigating the adverse impacts of cost transfers, thereby fostering more equitable and sustainable development trajectories.

A cross-sectional study on the key risk factors affecting farmers’ mental health

Background: Amidst fluctuating commodity prices and disrupted processing methods, farmers face escalating costs, including healthcare and property taxes. Disturbingly, the suicide rate among farmers is 3.5 times higher than that of the general population. This study addressed the critical gap in understanding the key risk factors influencing farmers’ mental health. The study aimed to identify the risk factors affecting farmers’ mental health, explore the root causes of their depression and anxiety, and provide essential management education tailored for them. Methods: A cross-sectional study was conducted with 153 farmers, collecting demographic details and conducting interviews to assess their financial status, health, and years of farming experience. Education on managing stress and anxiety was provided as part of the intervention. Results: From January to October 2023, 153 farmers (120 males, 30 females) were interviewed. Of these, 95 reported experiencing depression, with varying degrees of severity: 30.9% mild, 22.1% moderate, 10.1% moderate to severe, and 2% severe. The age group most affected was 36–50 years. Key risk factors identified were financial stress, smoking, and alcohol consumption. Conclusions: The study underscored significant public health concerns, revealing that factors such as age, working hours, and financial stress critically impact farmers’ mental health. Poor self-care, neglect of medical advice, and high rates of comorbidities among farmers highlight the need for targeted health interventions. Educating farmers on stress management and regular medical care is essential for improving their overall well-being, sustaining the agricultural workforce, and ensuring food security.

Depreciation and Trade Balance: An Exploration of the J-Curve Phenomenon in Indonesia

Research Originality: Global economic uncertainty has increased in recent years, leading to an appreciation of the US dollar relative to most global currencies, including the Indonesian rupiah. The novelty of this study is the inclusion of natural resource commodity price index variables, given the importance of these commodities in Indonesia's balance of trade.Research Objectives: This study aims to examine the effect of rupiah exchange rate depreciation on Indonesia's trade balance.Research Methods: This study uses a Vector Error Correction Model (VECM) to analyze Indonesia's balance of trade from January 2015 to December 2023, examining the potential J-Curve phenomenon resulting from the depreciation of the Indonesian rupiah.Empirical Result: Findings from the impulse response analysis suggest that the J-Curve phenomenon is present in Indonesia's balance of trade as a result of the rupiah's depreciation. The results of this study also confirm the positive effect of increasing natural resource commodity prices on Indonesia's trade balance.Implications: The implication of these findings is that exchange rate depreciation can improve Indonesia's trade balance only when natural resource commodity prices are rising. Conversely, if commodity prices decline, maintaining the stability of the rupiah exchange rate becomes crucial.JEL Classification: F11, F14, F31, Q02

Burp tax: Regulation and revenue potential in Indonesia

The increasingly urgent phenomenon of climate change requires concrete actions from various sectors, including the livestock sector which is a significant contributor to greenhouse gas emissions, particularly methane. This study explores the potential of implementing Burp Tax in Indonesia as a regulatory mechanism to reduce methane emissions from beef and dairy cattle farms. By referring to New Zealand's experience as a Burp Tax pioneer, this study analyzes the policy scheme that includes tax subjects, tax objects, tariffs, and fair and efficient collection mechanisms. In addition, this study identifies various obstacles that may be faced in the implementation of Burp Tax in Indonesia and provides recommendations to support the national commitment in achieving the Zero Emission target in 2045/2060. The research used qualitative methods with literature study data collection techniques. The research concludes that there is great potential for the imposition of Burp Tax, so that in addition to being a form of effort to reduce methane emissions from beef and dairy cattle farms, Burp Tax can also increase the portion of state revenue from the taxation sector. The specific tariff scheme is IDR30/KgCH4 with tax subjects in the beef and dairy cattle farming sector, including individuals with 30 cows and business entities. However, it is important to note that the imposition of Burp Tax may have an economic impact on livestock business actors and affect the price of meat and milk commodities. Therefore, further studies are needed regarding the potential and impact of Burp Tax implications in Indonesia. The results of this study are expected to make a significant contribution to the development of sustainable taxation policies that are responsive to the challenges of climate change.

Modelling the impact of increasing supplementary feed allowance on predicted sheep enterprise production, profit and financial risk across southern Australia

Context Supplementary feeding may alter sheep enterprise production and profit margin, but use may vary across regions, sheep breeds and mating seasons. Supplementary feeding is a means of ensuring adequate nutrition but increases operating costs. Modelling has previously indicated the most profitable sheep enterprises optimise stocking rate and target lamb production, whereas those that minimise supplementary feeding incur the least financial risk. Aims To explore the impact of increasing supplementary feed allowance on production, profit and financial risk. Methods Seventy-two sheep farm enterprises were simulated across eight southern Australian locations, including three breeds and three mating seasons. For each enterprise a low grain allowance (LGA) of 30 kg/head.year (threshold used in previous modelling) was compared to a high grain allowance (HGA) of 35 kg/head.year and 42 kg/head.year for Merino and non-Merino ewes (current industry recommendations), respectively. The financial risk of each enterprise was determined via Conditional Value at Risk of gross margins over 30 years, exploring downside risk in the worst 20% of scenarios. Key results A HGA increased production and profit in 32% of farm enterprises, but financial risk was often increased. Merino enterprises were generally the most profitable, least risky and consumed the greatest amount of supplementary feed, followed by Composite and then Maternal enterprises. Summer and autumn mating was often most profitable, but high supplement consumption in autumn-mated enterprises increased financial risk. Conclusions Increasing supplementary feeding may improve production and profit but may also increase financial risk using the parameters examined. Implications Producers may be able to improve the production, profit and financial risk of an enterprise through increased supplementary feeding, but this will be dependent on breed, input costs, commodity prices and location.

Assessing the economic and nutritional value of pollination services in Nepal

Pollination is a key ecosystem service crucial for supporting agricultural production, economic growth, social inclusion, and environmental protection. Understanding the economic value of pollination and its impact on human health and nutrition is essential for effective pollinator conservation and management. This study evaluates the economic and nutritional value of pollination services in Nepal and quantifies historic changes in pollinator reliance. Using public data on agricultural production and commodity prices, in combination with published nutritional composition values, we employ the dependency-ratio method to quantify economic and nutritional value across different regions of the country and through time. We conservatively estimate the annual economic value of pollination services in Nepal at US $477 million, representing 9% of total agricultural revenue. Pollinator-dependent crops, particularly fruits and vegetables are the source of essential nutrients; 40% of plant-based vitamin A and 14% of vitamin C are directly attributable to insect pollination. The cultivated area of these pollinator-dependent crops has increased by 91% in Nepal over 20 years – 3.7 times faster than equivalent increases in non-pollinator-dependent crops. The decline in wild pollinators during the same time period poses a threat, leading to potential pollination deficits and crop losses. Our study underscores the importance of conserving and managing pollinators to ensure sustainable agriculture, food security, and nutrition. Targeted efforts, including policy interventions and conservation strategies, are needed to safeguard pollinator populations and enhance pollination services.

ANALYZING THE IMPACT OF INDONESIAN SUSTAINABLE PALM OIL (ISPO) CERTIFICATION AND THE FACTORS THAT INFLUENCE CRUDE PALM OIL (CPO) EXPORTS VOLUME

This study examines the impact of Indonesian Sustainable Palm Oil (ISPO) certification on Indonesian Crude Palm Oil exports and other factors, such as commodity production and prices, exchange rates, and production levels. The data covers total CPO volume (HS codes 15111000 and 15119000) from January 2006 to December 2022, focusing on ISPO certification for large State-owned and Private Enterprises in palm oil cultivation and processing. The analysis employs the Difference-in-Differences (DID) method and multiple linear regression (MLR) to identify the linear relationship between the dependent and independent variables. The DID analysis revealed that Indonesian CPO production and global CPO prices significantly affected CPO export volumes, while soybean oil prices and effective exchange rates were not statistically significant in the regression model. The results provide valuable insights for policymakers and industry stakeholders in developing strategies that support sustainable growth, enhance competitiveness, and maintain Indonesia's leading position in the global palm oil market. Policymakers should focus on encouraging broader adoption of ISPO among palm oil producers through incentives, training, and technical support, especially for smaller producers.