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Behavioral Models for Energy with Applications

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (30 May 2022) | Viewed by 34043

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Special Issue Editors

Prof. Dr. Wing-Keung Wong

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Guest Editor

1. Department of Finance, Fintech & Blockchain Research Center, Big Data Research Center, Asia University, Taichung City 41354, Taiwan
2. Department of Medical Research, China Medical University Hospital, Taichung City 40447, Taiwan
3. Department of Economics and Finance, The Hang Seng University of Hong Kong, Hong Kong, China
Interests: behavioral models; mathematical modeling; econometrics; energy economics; equity analysis; investment theory; risk management; behavioral economics; operational research; decision theory; environmental economics; public health; time series analysis; forecasting
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Faridul Islam

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Guest Editor

Department of Economics, Morgan State University, Baltimore, MD 21251, USA
Interests: energy; mathamatical modelling; energy finance; energy pricing; carbon pricing; time series analysis; forecasting
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Dr. Husam Rjoub

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Guest Editor

Department of Business Administration, Faculty of Management Sciences, ILMA University, Karachi, Pakistan
Interests: International finance; asset pricing; energy economics; energy management and environmental sciences
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Dr. Aviral Kumar Tiwari

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Guest Editor

Indian Institute of Management Bodh Gaya, Bihar 824234, India
Interests: sustainability; SDGs; resource economics; environment and energy economics
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Special Issue Information

Dear Colleagues,

Behavioral models play a vital role in many fields in energy and natural resources and provides theories and tools that have been widely used in all areas of energy.

Most behavioral models are developed by using advanced mathematics, probability, knowledge of mathematics, probability, and statistics. Behavioral models are essential to develop theories in energy and natural resources and test their validity through the analysis of empirical real-world data.

A Special Issue of behavioral models in energy and natural resources with applications edited by Faridul Islam, Husam Rjoub, Aviral Kumar Tiwari, and Alan Wing-Keung Wong will be devoted to advancements in behavioral models with applications in different areas of energy and natural resources. This Special Issue will also bring together practical, state-of-the-art applications of mathematics, probability, and statistical techniques in energy and natural resources with applications.

This Special Issue aims to consider research on mathematical and statistical models with applications on, but not limited to, the following topics as research issues:

(i) domestic and international pricing of energy sources, such as oil, coal, gas and nuclear, hydrogen, wind, etc.;

(ii) modeling domestic and international carbon emissions prices;

(iii) modeling of financial returns and volatility of energy and natural resources;

(iv) analyzing the forecasting performance of energy commodities, natural resources and carbon emissions;

(v) analyzing the usefulness of inclusion of energy commodities, natural resources and carbon emissions as financial commodities in financial portfolios and optimal hedging (or insurance) of financial portfolios;

(vi) impacts on the environment and sustainability of pricing carbon emissions;

(vii) impacts on health and agriculture sector of (pricing) carbon emissions.

For example, authors could provide statistically valid prices for energy commodities, natural resources and carbon emissions using robust modeling techniques. Furthermore, authors could also explore financial returns, and volatility of energy commodities, natural resources and carbon emissions. Researchers are also encouraged to consider the energy commodities, natural resources and carbon emissions as financial commodities in financial portfolios and in optimal hedging (or insurance) of financial portfolios; evaluate the impacts on the environment and sustainability of pricing energy commodities, natural resources and carbon emissions; and evaluate the effects on health and agriculture costs of pricing carbon emissions, etc.

We invite investigators to contribute original research articles that advance the use of mathematics, probability, and statistics in the areas of energy and natural resources with applications. All submissions must contain original unpublished work not being considered for publication elsewhere.

Prof. Dr. Wing-Keung Wong
Dr. Faridul Islam
Dr. Husam Rjoub
Prof. Dr. Aviral Kumar Tiwari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

behavioral models
mathematics
probability
statistics
energy
applications
energy commodities
natural resources
carbon emissions
mathematical modeling
volatility modeling
dependence modeling
risk and portfolio modeling
forecasting

Published Papers (14 papers)

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Research

15 pages, 3462 KiB

Open AccessArticle

A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network

by Sarhang Sorguli and Husam Rjoub

Energies 2023, 16(6), 2844; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062844 - 18 Mar 2023

Cited by 4 | Viewed by 1585

Abstract

Energy accounting is a system for regularly measuring, analyzing, and reporting the energy use of various activities. This is done to increase energy efficiency and monitor the impact of energy usage on the environment. Primary energy accounting is now done by determining the amount of fossil fuel energy required to generate it. However, if fossil fuels become scarcer, this strategy becomes less viable. Instead, a new energy accounting approach will be required, one that takes into consideration the intermittent character of the two most prevalent renewable energy sources, wind and solar power. Furthermore, estimation of the energy consumption data collected from household surveys, whether using a recall-based approach or a meter-based one, remains a difficult task. Hence, this paper proposes a novel energy accounting model using Fuzzy Restricted Boltzmann Machine-Recurrent Neural Network (FRBM-RNN). The energy consumption dataset is preprocessed using linear-scaling normalization. The proposed model is optimized using the Adaptive Fuzzy Adam Optimization Algorithm (AFAOA). The performance metrics like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are estimated. The estimated results for our proposed technique are MSE (0.19), RMSE (0.44), MAE (0.2), and MAPE (3.5). Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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16 pages, 1706 KiB

Open AccessArticle

Does the Moderating Role of Financial Development on Energy Utilization Contributes to Environmental Sustainability in GCC Economies?

by Halmat Omer and Murad Bein

Energies 2022, 15(13), 4663; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134663 - 25 Jun 2022

Cited by 3 | Viewed by 1492

Abstract

This present research examined the association among carbon emissions, financial development, economic growth, natural resources, and energy usage in GCC nations within the environmental Kuznets curve framework by applying the datasets between 1995 and 2019. It used some empirical approaches, including second-generation unit roots and cointegration methods and method of moments quantile regression (MMQR). We detected a cointegrating interconnection between carbon emissions and financial development, energy usage, economic growth, natural resources, and squared of economic growth in the long term. Furthermore, the findings of the MMQR reveal that economic growth, financial development, energy usage, and natural resources degrade the environment, as well as proving the presence of the EKC hypothesis. Moreover, the results also demonstrated that financial development greatly moderates energy usage in order to attain environmental sustainability. Furthermore, the fixed-effect ordinary least squares, fully modified ordinary least squares, and dynamic ordinary least squares were also used in the study as a soundness check of the MMQR approach. The path of causality moves from financial development, economic growth, and squared of economic growth to CO₂ emissions. Lastly, the causality direction runs from carbon emissions to energy usage. Based on these findings, the energy mix of the region must be revised by ensuring the promotion of sustainable energy sources and other energy-efficient technology in order to attain the quality of the environment. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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12 pages, 276 KiB

Open AccessArticle

Modeling the Linkage between Vertical Contracts and Strategic Environmental Policy: Energy Price Marketization Level and Strategic Choice for China

by Ying Li, Wing-Keung Wong, Ming Jing Yang, Yang-Che Wu and Tien-Trung Nguyen

Energies 2022, 15(13), 4509; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134509 - 21 Jun 2022

Cited by 1 | Viewed by 1258

Abstract

The lower price of energy leads to higher coal consumption in China. The idea of an “environment-for-trade policy” could be used to achieve an international competitive advantage, which, in turn, has important implications. To address the issue, we develop properties to examine the link between the low price of energy and strategic environmental policy in China and investigate the choice of policy instruments in a strategic environmental policy model with vertical contracts. In addition, to contribute to the literature on strategic environmental policy, this paper also develops properties to investigate different choices of instruments for the environmental policy and includes the degree of energy marketization for the wholesale price in the study. To do so, we assume that the wholesale price of the polluting input increases with the market price. By using this assumption, this paper analyzes the effects of two instruments of the environmental policy on social welfare and concludes that there is no reason to expect both downstream and upstream firms to establish a high wholesale price. Due to the low level of marketization, when the government selects an emission tax as the policy instrument, the optimal tax rates should be higher than the marginal damage of emissions. However, the optimal resource tax is uncertain when its effect on environmental damage is taken into account. In other words, the resource tax is ineffective as a policy instrument. Our results can be used to draw some practical policies for countries to use their energy effectively. To promote energy sustainability, governments should liberate resource prices and reform the system to get efficient environmental policies. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

28 pages, 39718 KiB

Open AccessArticle

The Dynamic Spillover between Renewable Energy, Crude Oil and Carbon Market: New Evidence from Time and Frequency Domains

by Dan Nie, Yanbin Li, Xiyu Li, Xuejiao Zhou and Feng Zhang

Energies 2022, 15(11), 3927; https://0-doi-org.brum.beds.ac.uk/10.3390/en15113927 - 26 May 2022

Cited by 6 | Viewed by 1971

Abstract

To obtain the price return and price volatility spillovers between renewable energy stocks, technology stocks, oil futures and carbon allowances under different investment horizons, this paper employs a frequency-dependent method to study the dynamic connectedness between these assets in four frequency bands. The results show that, first, there is a strong spillover effect between these assets from a system-wide perspective, and it’s mainly driven by short-term spillovers. Second, in the time domain, technology stocks have a more significant impact on renewable energy stocks compared to crude oil. However, through the study in the frequency domain, we find renewable energy stocks exhibit a more complex relationship with the other two assets at different time scales. Third, renewable energy stocks have significant spillover effect on carbon prices only in the short term. On longer time scales, other factors such as energy prices, climate and policy may have a greater impact on carbon allowance prices. Fourth, the spillover effect of the system is time-varying and frequency-varying. During the European debt crisis, the international oil price decline and the COVID-19 pandemic, the total spillover index of the system has experienced a substantial increase, mainly driven by medium, medium to long or long term spillovers. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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18 pages, 4123 KiB

Open AccessArticle

Integration of Kouprey-Inspired Optimization Algorithms with Smart Energy Nodes for Sustainable Energy Management of Agricultural Orchards

by Pannee Suanpang, Pattanaphong Pothipassa, Kittisak Jermsittiparsert and Titiya Netwong

Energies 2022, 15(8), 2890; https://0-doi-org.brum.beds.ac.uk/10.3390/en15082890 - 14 Apr 2022

Cited by 6 | Viewed by 2119

Abstract

Energy expenditures are now the main cost for two businesses that generate huge incomes each year for Thailand, which are agribusiness and community tourism. As entrepreneurs have to share a portion of their income as energy utility bills each month. This is a factor which results in them getting a low net return. Recognizing the need for energy management for sustainable use in agriculture focusing on durian cultivation in Kantharalak district and community tourism in Sisaket province, this research used a newly developed optimization algorithm called Kouprey-inspired optimization (KIO) to assist energy management in smart agriculture to support community-based tourism. This was initiated with a smart energy node to reduce the energy and labor costs for volcanic durian planting and accommodation in community-based tourist attractions in Sisaket province. The results showed that the combination of the KIO algorithm and smart energy node allowed for efficient management of the volcanic durian orchards and the use of clean energy in combination with traditional electric power for volcanic durian cultivation and community-based tourism. As the research area in Sisaket province had eight hours of solar power per day, this was sufficient for smart agriculture and community-based tourism in the daytime and in the evening. Furthermore, this allowed operators in both the agricultural and tourism sectors to reduce the labor costs of the durian orchard business and community-based tourism by about 30%, and in the energy sector, the costs could be reduced by 50%. As a consequence, this prototype would lead to the expansion and trial in durian orchards in the Eastern Economic Corridor area, which is an important economic area producing durian for export of the country. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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21 pages, 3729 KiB

Open AccessArticle

Risk Contagion between Global Commodities from the Perspective of Volatility Spillover

by Hong Shen, Qi Pan, Lili Zhao and Pin Ng

Energies 2022, 15(7), 2492; https://0-doi-org.brum.beds.ac.uk/10.3390/en15072492 - 28 Mar 2022

Cited by 2 | Viewed by 1502

Abstract

Prices of oil and other commodities have fluctuated wildly since the outbreak of the COVID-19 pandemic. It is crucial to explore the causes of price fluctuations and understand the source and path of risk contagion to better mitigate systemic risk and maintain economic stability. The paper adopts the method of network topology to examine the path of risk contagion between China’s and foreign commodities, focusing on the dynamic evolution and transmission mechanism of risk contagion during the pandemic. This research found that among China’s commodities, energy, grain, and textiles are net recipients of risk contagion, while chemical products and metals are net risk exporters. Among international commodities, industries have positive risk spillover effects on metals and textiles. During the first phase of the pandemic, China’s commodities were the main exporters of risk contagion. However, international industries and metals became the main risk exporters and exerted risk spillover on China’s commodities in the second phase of the pandemic. Moreover, based on total volatility spillover index of commodities, the risk contagion among the commodities follows three paths: “interest rate → commodities → money supply”, “China’s economic expectation → commodities → foreign economic expectation”, and “commodities → consumer confidence”. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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18 pages, 1901 KiB

Open AccessArticle

Which Factors Determine CO₂ Emissions in China? Trade Openness, Financial Development, Coal Consumption, Economic Growth or Urbanization: Quantile Granger Causality Test

by Zhenkai Yang, Mei-Chih Wang, Tsangyao Chang, Wing-Keung Wong and Fangjhy Li

Energies 2022, 15(7), 2450; https://0-doi-org.brum.beds.ac.uk/10.3390/en15072450 - 26 Mar 2022

Cited by 41 | Viewed by 2247

Abstract

The current study employs a Granger causality test within a Quantile approach investigating CO₂ emission determinants in China. Results show urbanization, financial development and openness to trade are leading determinants of CO₂ emissions in China. These results highlight climate change issues while taking advantage of a new methodology to fill a gap in the current literature. Our findings show key implications for PRC government policy related to pollutant reduction policy. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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13 pages, 17423 KiB

Open AccessArticle

Autonomous Energy Management by Applying Deep Q-Learning to Enhance Sustainability in Smart Tourism Cities

by Pannee Suanpang, Pitchaya Jamjuntr, Kittisak Jermsittiparsert and Phuripoj Kaewyong

Energies 2022, 15(5), 1906; https://0-doi-org.brum.beds.ac.uk/10.3390/en15051906 - 04 Mar 2022

Cited by 6 | Viewed by 2712

Abstract

Autonomous energy management is becoming a significant mechanism for attaining sustainability in energy management. This resulted in this research paper, which aimed to apply deep reinforcement learning algorithms for an autonomous energy management system of a microgrid. This paper proposed a novel microgrid model that consisted of a combustion set of a household load, renewable energy, an energy storage system, and a generator, which were connected to the main grid. The proposed autonomous energy management system was designed to cooperate with the various flexible sources and loads by defining the priority resources, loads, and electricity prices. The system was implemented by using deep reinforcement learning algorithms that worked effectively in order to control the power storage, solar panels, generator, and main grid. The system model could achieve the optimal performance with near-optimal policies. As a result, this method could save 13.19% in the cost compared to conducting manual control of energy management. In this study, there was a focus on applying Q-learning for the microgrid in the tourism industry in remote areas which can produce and store energy. Therefore, we proposed an autonomous energy management system for effective energy management. In future work, the system could be improved by applying deep learning to use energy price data to predict the future energy price, when the system could produce more energy than the demand and store it for selling at the most appropriate price; this would make the autonomous energy management system smarter and provide better benefits for the tourism industry. This proposed autonomous energy management could be applied to other industries, for example businesses or factories which need effective energy management to maintain microgrid stability and also save energy. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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33 pages, 3169 KiB

Open AccessArticle

Empirical Study on CO₂ Emissions, Financial Development and Economic Growth of the BRICS Countries

by Fangjhy Li, Yang-Che Wu, Mei-Chih Wang, Wing-Keung Wong and Zhijie Xing

Energies 2021, 14(21), 7341; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217341 - 04 Nov 2021

Cited by 36 | Viewed by 2194

Abstract

This paper empirically examined relevant data on BRICS CO₂ emissions, financial development, and economic growth in the past 40 years, and analyzed the correlation between them. Using the cointegration test, it found that there is a clear correlation between the variables in China and South Africa, which show that there is a two-way relationship between CO₂ emissions, financial development, and economic growth in both countries. Using the quantile regression method in the analysis, the results demonstrated that at the 0.6th quartile, South Africa’s financial development had a negative impact on CO₂ emissions, while Brazil’s CO₂ emissions had a negative impact on financial development. Economic growth was subsequently added as a control variable, and the quantile-on-quantile regression method was used to test the correlation between the financial development of the BRICS countries and their CO₂ emissions. Finally, based on empirical conclusions, this paper proposed that BRICS countries should focus on sustainable economic development; when government departments formulate emission-reduction policies, they must reasonably consider the relationship between financial development and emission-reduction policies. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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12 pages, 3198 KiB

Open AccessArticle

A Note on Forecasting the Historical Realized Variance of Oil-Price Movements: The Role of Gold-to-Silver and Gold-to-Platinum Price Ratios

by Rangan Gupta, Christian Pierdzioch and Wing-Keung Wong

Energies 2021, 14(20), 6775; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206775 - 17 Oct 2021

Cited by 12 | Viewed by 1689

Abstract

We examine the predictive value of gold-to-silver and gold-to-platinum price ratios, as proxies for global risks affecting the realized variance (RV) of oil-price movements, using monthly data over the longest available periods of 1915:01–2021:03 and 1968:01–2021:03, respectively. Using the two ratios, we find statistically significant evidence of in-sample predictability for increases in RV for both ratios. This finding also translates into statistically significant out-of-sample forecasting gains derived from these two ratios for RV. Given the importance of real-time forecasts of the volatility of oil-price movements, our results have important implications for investors and policymakers. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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17 pages, 807 KiB

Open AccessArticle

The Effect of Energy Consumption and Economic Growth on Environmental Sustainability in the GCC Countries: Does Financial Development Matter?

by Hala Baydoun and Mehmet Aga

Energies 2021, 14(18), 5897; https://0-doi-org.brum.beds.ac.uk/10.3390/en14185897 - 17 Sep 2021

Cited by 38 | Viewed by 2937

Abstract

Achieving environmental sustainability whilst minimizing the climate change effect has become a global endeavor. Hence, this study examined the effect of energy consumption, economic growth, financial development, and globalization on CO₂ emissions in the Gulf Cooperation Council (GCC) countries. The research utilized a dataset stretching from 1995 to 2018. In a bid to investigate these associations, the study applied cross-sectional dependence (CSD), slope heterogeneity (SH), Pesaran unit root, Westerlund cointegration, cross-sectionally augmented autoregressive distributed lag (CS-ARDL), and Dumitrescu and Hurlin (DH) causality approaches. The outcomes of the CSD and SH tests indicated that using the first-generation techniques produces misleading results. The panel unit root analysis unveiled that the series are I (1). Furthermore, the outcomes of the cointegration test revealed a long-run association between CO₂ emissions and the regressors, suggesting evidence of cointegration. The findings of the CS-ARDL showed that economic growth and energy consumption decrease environmental sustainability, while globalization improves it. The study also validated the environmental Kuznets curve (EKC) hypothesis for GCC economies. In addition, the results of the DH causality test demonstrated a feedback causality association between economic growth and CO₂ emissions and between financial development and CO₂ emissions. Moreover, there is a one-way causality from energy consumption and globalization to CO₂ emissions in GCC economies. According to the findings, environmental pollution in GCC countries is output-driven, which means that it is determined by the amount of energy generated and consumed. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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12 pages, 639 KiB

Open AccessArticle

Another Look into the Relationship between Economic Growth, Carbon Emissions, Agriculture and Urbanization in Thailand: A Frequency Domain Analysis

by Mário Nuno Mata, Seun Damola Oladipupo, Rjoub Husam, Joaquim António Ferrão, Mehmet Altuntaş, Jéssica Nunes Martins, Dervis Kirikkaleli, Rui Miguel Dantas and António Morão Lourenço

Energies 2021, 14(16), 5132; https://0-doi-org.brum.beds.ac.uk/10.3390/en14165132 - 19 Aug 2021

Cited by 6 | Viewed by 2552

Abstract

This empirical study assesses the effect of CO₂ emissions, urbanization, energy consumption, and agriculture on Thailand’s economic growth using a dataset between 1970 and 2018. The ARDL and the frequency domain causality (FDC) approaches were applied to assess these interconnections. The outcome of the bounds test suggested a long-term association among the variables of investigation. The ARDL outcomes reveal that urbanization, agriculture, energy consumption, and CO₂ emissions positively trigger Thailand’s economic growth. Additionally, the frequency domain causality test was used to detect a causal connection between the series. The main benefit of this technique is that it can detect a causal connection between series at different frequencies. To the understanding of the authors, this is the first study in the case of Thailand that will apply the FDC approach to capture the causal linkage between GDP and the regressors. The outcomes of the causality test suggested that CO₂ emissions, urbanization, energy consumption, and agriculture can predict Thailand’s economic growth in the long term. These outcomes have far-reaching implications for economic performance and Thailand’s macroeconomic indicators. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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12 pages, 1329 KiB

Open AccessArticle

Modeling the Dynamic Linkage between Renewable Energy Consumption, Globalization, and Environmental Degradation in South Korea: Does Technological Innovation Matter?

by Tomiwa Sunday Adebayo, Manuel Francisco Coelho, Dilber Çağlar Onbaşıoğlu, Husam Rjoub, Mário Nuno Mata, Paulo Viegas Carvalho, João Xavier Rita and Ibrahim Adeshola

Energies 2021, 14(14), 4265; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144265 - 14 Jul 2021

Cited by 57 | Viewed by 3127

Abstract

The present research assesses the influence of globalization and technological innovation on CO₂ emissions in South Korea as well as taking into account the role of renewable energy consumption and energy consumption utilizing datasets between 1980 and 2018. The autoregressive distributed lag (ARDL) bounds testing method is utilized to assess long-run cointegration. The outcome of the ARDL bounds test confirmed cointegration among the series. Furthermore, the ARDL reveals that economic growth, energy consumption and globalization trigger environmental degradation while technological innovation improves the quality of the environment. In addition, the study employed the frequency domain causality test to capture causal linkage among the series. The major advantage of this approach is that causal linkage between series can be captured at the short, medium and long term, respectively. The outcomes of the causality test revealed that globalization, technological innovation, economic growth and energy use can predict CO₂ emissions in South Korea. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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13 pages, 2570 KiB

Open AccessArticle

The Causal Linkage between Energy Price and Food Price

by Dervis Kirikkaleli and Ibrahim Darbaz

Energies 2021, 14(14), 4182; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144182 - 11 Jul 2021

Cited by 16 | Viewed by 3180

Abstract

This paper aims to reveal the causal relationship between energy prices and food prices and whether this relationship is similar in the food sub-groups forming the food price index used. As food prices more than doubled during the 2008 economic crisis, this relationship has received considerable attention from researchers. Many researches have been conducted to determine the causes and consequences of the 2008 food price crisis. Researches are mainly focused on crude oil and bio-energy in terms of “energy”. This research is not only differentiated by the data used but also by the methodology employed. The study attempts to add new findings to the empirical food price literature by utilizing relatively newly developed methods, namely Toda–Yamamoto causality, Fourier Toda–Yamamoto causality, and spectral BC causality tests. The spectral BC causality test clearly reveals that there is bidirectional causality between the energy price index and food price indexes (grains, other food, and oils) at different frequencies. Full article

(This article belongs to the Special Issue Behavioral Models for Energy with Applications)

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