Assessing financial factors for oil supply disruptions and its impact on oil supply security and transportation risks

Abstract

The evaluation of energy security offers a standard for policy research and highlights the problems of securing the energy supply. A composite index for analyzing the risk of Southeast Asian nations’ oil supply is developed in this study. Indicators used to calculate the index include the import-to-LGE ratio, GPR, market liquidity, gross domestic product, the import-to-consumption ratio, heterogeneity, oil price volatility, US$ volatility, and transportation risk. The index is based on these and other factors. According to the findings, Nepal and Sri Lanka are the most susceptible to oil supply interruptions. This indicates that India is more likely to shift its oil suppliers. At the same time, Maldives, Nepal, and Sri Lanka have the lowest supply risk scores, indicating that they are the most vulnerable to supply disruptions. Reduce the effect of oil supply risk by enacting policies such as the adoption of renewable technologies, nuclear power generation, diversification of exporting supplies, and reducing fossil fuel subsidies.

Introduction

The influence of the local and international political and financial atmosphere on the economy, industries, and market behavior has become a focal point of study in the context of an increasingly interconnected globalized trade (Zameer et al. 2021). Recent events, such as the turbulence in global economic industries caused by the COVID-19 outbreak, Britain’s formal withdrawal from Asia, and the escalation of global trade frictions, have only served to heighten this sense of unpredictability (Yasmeen et al. 2020). As a result, scholars have begun to pay more attention to how political uncertainty or political risk can affect economic activities (Zameer et al. 2020b). Additionally, it has been emphasized that geopolitical risk has a role in shaping economic cycles and trends between nations that disrupt the otherwise calm and orderly development of global relations are all examples of geopolitical risks (Wang et al. 2019). Terrorist acts are only one kind of geopolitical risk, conflicts in important locations, political disputes over taxation and state spending, the continuous trade friction between Nepal and India, and so on. Studies of the relationship between conflicts and economies have been informed by actual observations of the impact that GPRs have on economies. Existing research has mainly examined the influences of economic growth on the environment, using both linear and non-linear estimation techniques, because environmental quality changes across the stages of the economy’s development. The EKC framework proposed by Grossman and Krueger has been widely used in the aforementioned studies, with the primary objective of examining the relationship between economic development and environmental quality (Shahbaz et al. 2020). This theory proposes that scale and composition effects contribute to environmental degradation at the outset of economic development in an underdeveloped economy (Zameer et al. 2020a). For instance, during this period of rapid development, industrialization often occurs, leading to a spike in demand for energy, especially fossil fuels, in the context of developing nations, which in turn has negative effects on the environment. However, once economic growth reaches a certain level, the technique effect kicks in, effectively canceling out the trade-off between economic expansion and environmental degradation through the medium of technological progress (Qu et al. 2022). The EKC hypothesis is a set of postulates that advocates for a connection between GDP growth and environmental quality in the form of an inverted U. Several other macroeconomic variables, such as natural resource consumption, technological innovation, globalization, human capital, and financial development, are hypothesized to influence environmental quality.

The treadmill theory of production, endogenous growth theory, and globalization theory of growth can all be used to make sense of the interconnections between technological progress, natural resource availability, international trade, and environmental quality. The treadmill theory of production postulates that using natural resources to make goods is the root cause of environmental degradation (Huang and Liu 2021). To promote economic growth, it is important to make use of natural resources (Han et al. 2022). However, overuse of certain natural resources, such as main fossil fuel sources, may have unfavorable ecological effects, as measured by an increase in ecological footprint. In contrast, limiting the expansion of ecological footprints via the use of natural resources that are easier on the planet may have a significant impact on environmental quality.

Such political news has global repercussions, reinforcing the view that political risk is a critical component in determining the direction of various asset markets. As a result, it is crucial to consider an investment’s ability to withstand geopolitical risks (Jin et al. 2022). According to a 2017 Gallup poll reflecting the opinions of over a thousand financial backers, a majority of respondents (75%) expressed concern about the global economy as a result of the many militaries and diplomatic crises taking place throughout the globe. Together with economic, macroeconomic, and economic strategy uncertainty, GPRs may have severe adverse impacts on asset price dynamics (Xu et al. 2022).

Proponents argue that economic restrictions are the primary obstacle to implementing sustainability in the energy industries; this is a problem that could be exacerbated by seeping geopolitical risks, which would in turn make it more difficult for the green transformation to accelerate its goal of sustainable development (Zhang et al. 2022). The growth of rich and emerging nations’ stock and credit markets are the best predictors of their economies’ overall financial progress. On the other hand, climatic and geopolitical threats make the equities and bond markets, including green bonds industries, very unstable (Hosseini et al. 2013). Given this setting, we want to calculate how green equities and green bonds will react to different global geopolitical threats. There are two ways in which green markets might be affected by geopolitical uncertainty.

To start, the price of hydrocarbons is a major indirect avenue via which geopolitical risk may affect green markets. A large body of recent empirical research shows that fluctuations in oil prices have a negative correlation with green stock and green bond values (Mohsin et al. 2019). Numerous studies show that the oil price is vulnerable to geopolitical concerns (Ikram et al. 2019). The crude oil industry is also impacted by geopolitical risk through the supply and demand channels, as stated. Rising oil costs, however, could boost green investments through the substitution effect since the research suggests that renewable or cleaner energy may replace dirty or carbon-content resources (Iqbal et al. 2019).

Studies of the relationship between conflicts and economies have been informed by actual observations of the impact that GPRs have on economies. Many researchers have tried to dissect the impact of various geopolitical events on financial markets, and such studies have received a lot of attention (Mohsin et al. 2020). Many countries in the present century have undergone civil war, international conflict, political instability, and terrorist attacks; in fact, one-third of all nations have been affected by civil wars alone. Changes in leadership, administration or the occurrence of political upheavals, civil unrest, or more violent incidents like terrorist attacks are all examples of events with the potential to disrupt economic performance and asset markets (Agyekum et al. 2021).

Traditional methods of aggregating indicators were employed in earlier research, which concentrated on particular elements of indicator selection. Furthermore, they failed to account for the relative weights of the various indicators, which makes the findings difficult to trust. A composite indicator technique for assessing oil supply risk has never been used in any prior research. The methodological approach for quantitative risk assessment in oil disruptions is the topic of this study. Many standards are developed, and the interrelationships among all indexes are evaluated and explored. These factors are then combined into (CI) using an integer programming strategy, with constraints on the weights assigned to each sub-indicator. In contrast to other studies, this one examines the oil supply risk in Southeast Asia holistically and adds significantly to the body of knowledge. In addition, we have included an empirical estimate of the interruption of oil supplies in our contribution. Although most models presume that local oil output is unaffected by supply interruption, our research reveals that this assumption has major consequences for oil supply interruption. While several indicators, such as “country-specific” regulations, critical gasoline resources, foreign currency reserves, and environmental exposures that potentially impact the sources of risk of oil supply, have not been studied because of data unavailability.

Literature review and hypothesis development

This section is further separated into two major sub-sections in which the previous one discusses the theoretical framework, while the concluding reviews the related empirical literature.

Theoretical underpinning

According to the endogenous growth hypothesis, emerging countries may improve their economic and environmental well-being via technological innovation that is realized (Iqbal et al. 2022). In addition, technical advancement might be seen as the impetus needed to alter the economic and industrial structure of emerging nations to make better use of natural energy. Energy-use-related emissions may be greatly reduced if technology advancements in the energy sector catalyze the shift from the use of dirty to clean energy sources. Therefore, it is widely understood that public investment in R&D for clean energy development improves environmental well-being (Asbahi et al. 2019). According to this theory, the expansion of the ecological footprints of emerging economies may be slowed by technical innovation via the medium of the clean energy transition.

Globalization, in its many forms, may be beneficial to a country’s economic development, but it also has a wide range of environmental impacts, as the globalization theory of growth acknowledges. For instance, as a result of trade liberalization, more nations are engaging in cross-border commerce to increase their value added. However, there are distinctions among trade partners in terms of environmental consequences (Xia et al. 2020). There is a common misconception that countries with stricter environmental restrictions may take advantage of those with laxer ones via international commerce. Additionally, nations that rely heavily on fossil fuels tend to excel in polluting production methods and eventually become net exporters of polluting products. As a result, it is reasonable to assume that globalization of commerce has unintended ecological implications in these nations (Shah et al. 2019). By contrast, nations that do not rely on fossil fuels for their economies should see good environmental consequences as a result of globalization, thanks to their ability to specialize in and export cleaner commodities.

Alternatively, it has been argued that improvements in human capital, particularly via the channels of investments in health and education, might affect environmental quality (Mohsin et al. 2020). Human capital development, for instance, may raise people’s environmental consciousness, which in turn motivates them to adopt eco-friendly purchasing habits that have a beneficial effect on the environment. Investment in education has been postulated to encourage energy consumers to utilize greener energy sources and switch to cleaner energy options, which would significantly reduce (Xiuzhen et al. 2022). Moreover, it might ultimately help in developing technical advances and employing defend qualities. A further tenet of endogenous growth theory is that investments in R&D and human capital go hand in hand. Therefore, it may be predicted once again that improving human capital can improve the environment by way of technical innovation. Finally, it is argued that an economy’s financial system has a significant impact on its environmental quality. An undeveloped financial system may be harmful to environmental quality, although it is expected to increase economic development. Pollution-heavy companies, for instance, may find it easier to get financing from the less-developed financial systems of developing nations, where the money borrowed may then be used to support practices that are harmful to the environment (Ullah et al. 2020). Green finance, which may give loans to businesses eager to invest in energy-efficient industrial processes, is one area where a sophisticated financial system might be advantageous (Agri et al. 2018). Economic growth may also have a significant influence on funding R&D programs that seek to advance environmental technology via innovation.

Empirical evidence

Below, we provide a historical overview of the empirical research that has examined the impacts of natural resource usage, technological innovation, globalization, economic growth, human capital, and financial development on the ecological footprint in the context of emerging nations.

Nexus between technological innovation and environment

Due to the small number of research examining the connection between technological innovation and ecological footprint in the setting of developing nations, the literature on the topic is scant. Most of these studies have emphasized the role that improved technology plays in reducing ecological footprint expansion by facilitating more effective resource management (Nhuong and Quang 2022). After analyzing 280 Chinese cities to determine the correlation between technological advancements and environmental impact, the results of this research show that technological progress enhances ecological quality by lowering the tally of environmental harm. Similar findings were reached by Ren et al. (2022) about the need for technology advancement in the mitigation of rising countries’ ecological impact. observed similar results for the BRICS nations. According to the authors, these developing countries’ ecological footprints might be reduced with the aid of emerging environmental technology. But, Darling et al. (2022) argued that technological progress harms the environment in the member states of the Asia–Pacific Economic Cooperation (APEC). The authors argued that APEC nations should embrace cutting-edge technology to speed up the process of industrialization; nevertheless, it has been shown that such developments in technology also have negative effects on the natural world. In conclusion, it is possible to state that a consensus has not yet been achieved in the literature about the effects of technological developments on the ecological footprint.

Nexus between globalization and environment

In summarizing the research on the relationship between globalization and developing countries’ ecological footprint, Ahmad et al. (2019) reviewed about 100 and above studies and concluded that development is a double-edged sword with uncertain environmental effects. According to Maithya et al. (2022), globalization promotes environmental deterioration by raising Turkey’s ecological footprint. reached a similar conclusion for the emerging countries of South Asia, arguing that the nations’ ecological footprints have become worse in tandem with the economy’s increasing involvement in globalization. Using yearly data from 1971 to 2014, however, concluded that globalization had a positive effect on Egypt’s ecological footprint. Diniz et al. (2022) drew parallels between the globalization process and the efforts of Belt and Road Initiative nations to lower their ecological impact over the long term. On the other hand, Ahmadian-Yazdi et al. (2022) looked into the link between financial globalization and ecological footprints and discovered that the former enhances environmental well-being by reducing long-term ecological footprint estimates in the context of developing countries. Relatedly, found that financial globalization may explain the discrepancies in the ecological footprint level of these nations, although economic and trade globalization cannot. This was based on an analysis of 14 MENA economies. made a similar point, arguing that developing nations see an increase in their ecological footprint as a result of financial globalization-induced FDI inflows since their environmental policies are ineffective.

Nexus between economic growth and environment

Since the groundbreaking work of Nassani et al. (2019), the EKC hypothesis has provided a theoretical framework for exploring the connection between real income growth and environmental deterioration. Most of the research that has backed up the EKC theory has noted that once an economy achieves a certain level of development, it can control the rise in the ecological footprint statistics, something it could not do when its economic growth was slower. Using ecological footprint as a surrogate for environmental quality, used the generalized method of moments (GMM) strategy and found support for the EKC hypothesis across an assemblage of developing countries over the long term, who conducted a similar analysis for five Southeast Asian countries and also found that economic growth increases ecological footprint figures monotonically; however, they concluded that the EKC hypothesis was correct because the predicted elasticity parameter for the squared term of economic growth was relatively smaller. However, the EKC hypothesis has been disputed by several earlier research, which have indicated that it does not apply to all economies. Therefore, this research indicated that it may not be able to lower the ecological footprint level even after attaining the level of economic development necessary to do so and that continued economic growth might lead to a larger ecological footprint level instead. According to Khan et al. (2020), the EKC theory for ecological footprint does not apply to Tunisia because of the U-shaped link between economic development and environmental deprivation in the country. However, discovered a U-shaped relationship between China’s and India’s economic growth and ecological footprint, suggesting that the EKC hypothesis’s veracity has yet to be shown for these emerging countries. Similarly, research in China rejected the EKC theory for the same reason.

Nexus between human capital and environment

Charnes et al. (1978) utilized annual data to estimate the connection within the context of developing nations. Based on the data, it seems that improvements in human capital only have a negligible effect on the national ecological footprint in the near term. To be more specific, the authors argued that investing in education to increase human capital can raise people’s consciousness about the dangers of environmental degradation, which in turn can lead to better resource management and preservation. Gilbertson et al. (2012) found that investing in human capital reduced South Africa’s and China’s respective long-term ecological footprint figures. Renault et al. (2017) looked at the situation in India, another growing country, and found similar evidence that increasing human capital led to a smaller ecological footprint. In addition, the authors determined that human capital had a direct and unambiguous impact on the estimated ecological footprint. However, found a negative in a survey of 13 MENA nations. Human capital growth, the authors observed, is linked to greater economic potential and social well-being, both of which are likely to increase ecological demand and the resulting footprint. Therefore, on the environment are unknown.

Nexus between financial development and environment

In the existing literature, particularly in the context of rising nations, it has been demonstrated that there are several natural elements associated with financial development. Financial expansion may raise the environmental footprints of industrialized nations, but it may reduce the footprints of less-developed countries over the long run (He et al. 2019). For this reason, the authors suggested that achieving ecological sustainability in underdeveloped countries is more reliably accomplished by bolstering the credibility of the banking sector than in affluent nations. The researchers hypothesized that the negative correlation between economic development and environmental cost in developing countries could be accounted for by the underdeveloped economic industries of these nations; consequently, they concluded that even moderate development in the economic industry does not lead to a corresponding increase in environmental footprints. In contrast, Fang et al. (2021) indicated that economic development is bad for Qatar’s environment since it increases the nation’s immediate and long-term environmental footprint. To examine the link between GDP development and environmental cost, a group of researchers from 11 developing nations utilized the augmented mean group (AMG) estimator (Bertoldi and Mosconi 2020). Singapore had a positive correlation between economic growth and environmental footprint, whereas China and Vietnam had a negative correlation, and India, Brazil, the Philippines, Mexico, South Korea, South Africa, Bangkok, and Istanbul had no important correlation at all. New evidence reveals, however, that the environmental costs of low-, middle-, and high-income nations are largely unaffected by financial expansion. Therefore, studies that have shown economic growth to be negative for the environment have typically concluded that it may lead to a larger demand for environmental reserves and that the use of these resources might impose higher chances of rising environmental footprint statistics.

Data and method

The GRA-SRA energy poverty index method

Because many indexes are used to measure energy poverty, there is no one standard to measure it, making a quantitative assessment of energy poverty even more critical. Thus, the authors have devised an all-encompassing index to measure energy crises and simultaneously incorporate the current situation when analyzing norms and indexes for assessing energy poverty. As a result, the multifunctional energy poverty complete index is a broad measure that depicts home energy consumption effectiveness and purity. China’s unique energy import, energy supply development, domestic energy spending, and the total quantity of cookery implements were all analyzed by Verhoef et al. (2015). As a result, this study employs both GRA and SRA methods to evaluate the power index.

The indicator indicates a nation’s actual performance throughout the evaluation phase. An approximation of the values of factors for multifunctional energy poverty is made when two parameters have a close link to each other. A higher percentage of weight is given to the indices that have the greatest number of significant correlations. The GRA method calculates connection levels when assigning weights, while SRA employs a balanced price strategy. After that, the indication weights and numbers are combined to produce an oil extent of poverty using a simple linear weighting technique. Because of this, the basic idea of GRA is to establish similarities between various indicators and reference indices to verify the tightness of a connection. When it comes to the gray interference pattern among the hands, geometrical (GRA) (Porter 1980).

1. When it comes to a gray interference pattern among the indicators, geometrical (GRA) Furthermore, the comparison series displays the effectiveness of signal k over n assessment phases (defined), while the comparison series reflects the efficacy of indication I over n assessment phases (denoted). The following approaches are used to determine the GRA level between the variables. GRA may be calculated using these steps. Calculating the mean image (or initial image) of X₀ and X_i, i = 1, 2, …, m.

   where

   $$X_i^{\prime }=\frac{X_i}{x_i\left(1\right)}=\left(x_i^{\prime },\left(1\right),,,x_i^{\prime },\left(2\right),,,x_i^{\prime },\left(3\right),,,\cdots ,,,x_i^{\prime },\left(n\right)\right);i=0,1,2,\cdots ,m$$ 1
2. Calculating the difference sequences of i = 1, 2, …, m as

   $${\mathrm{\Delta }}_j\left(k\right)=\left|x_0^{\prime },\left(k\right),-,x_i^{\prime },\left(k\right)\right|,\mathrm{\Delta }=\left({\mathrm{\Delta }}_i,\left(1\right),,,{\mathrm{\Delta }}_i,\left(2\right),,,{\mathrm{\Delta }}_i,\left(3\right),,,\cdots ,,,{\mathrm{\Delta }}_i,\left(n\right)\right),i=1,2,\cdots ,m$$ 2
3. Ascertaining the maximum and minimum variances

   $$\begin{array}{c}M={\max }_i{\max }_k{\mathrm{\Delta }}_i\left(k\right)\\ m=\mathrm{minim}\mathrm{ink}\mathrm{\Delta }i\left(k\right)\end{array}$$ 3
4. Computing gray relational coefficients by

   $$Y_{0i}\left(k\right)=\frac{m+\xi M}{\mathrm{\Delta }i\left(k\right)+\xi M};\xi \in \left(0,1\right);k=1,2,3,\cdots ,n;i=1,2,3,\cdots ,m$$ 4

   where ξ is the differentiating coefficient, which is estimated to be 0.5 in the literature.
5. Computing the gray relational grade by

   $$Y_{0i}=\frac{1}{n}{\sum }_{k=1}^n\left(Y_{0i},\left(k\right)\right);i=1,2,\cdots ,m_2^{\prime }$$ 5

   where 1/n can be substituted by the weights wk, as in the following; if the effect of each factor is different, where ${\sum }_k^n=1$

   $$Y_{0i}=\frac{1}{n}{\sum }_{k=1}^n\left(Y_{0i},\left(k\right),.,w_k\right);i=1,2,\cdots ,m$$ 6
   Therefore, 1/n means the criteria weights are equally distributed, and Wk implies that the consequences are unevenly distributed, which resembles a common actual life scenario. Further discussion is provided by the differentiation of the dual GRA equations. Because all of the input conditions had equal values, we conclude that Deng’s Greg Analyses is a perfect starting point (He et al. 2020). The dual directional absolute GRG is computed using the following procedure for the two variable sets X_i and X_j.

   1. Placing the set of data with associated mirrored sequencing together with normalization.
   2. Making sure that the data numbers vary between 0 and 1 in each sequence.
   3. Determining the specific set of zero-starting line pictures.
   4. Calculating |s_i|, |s_j|, and |s_j–s_i|.
   To determine pure GRG (), the following steps must be followed:

   $${\epsilon }_{\mathit{ij}}=\frac{1+\left|s_i\right|+\left|s_j\right|}{1+\left|s_i\right|+\left|s_j\right|+\left|{s_j-s}_i\right|}$$ 7

6. Calculating the dual directional absolute.

GRG (ε ±), as

$${\epsilon }^{\pm }=\left\{+,\max \right)\left({\epsilon }_{\mathit{ij}},,,{\epsilon }_{\mathit{ij}}^n\right);\mathrm{\Delta }={\epsilon }_{\mathit{ij}}-{\epsilon }_{\mathit{ij}}^n>0$$ 8

$${\in }^{\pm }=\left\{\begin{array}{c}+\max \left({\epsilon }_{\mathit{ij}},,,{\epsilon }_{\mathit{ij}}^n\right);\mathrm{\Delta }={\epsilon }_{\mathit{ij}}-{\epsilon }_{\mathit{ij}}^n>0\\ -\max \left({\epsilon }_{\mathit{ij}},,,{\epsilon }_{\mathit{ij}}^n\right);\mathrm{\Delta }={\epsilon }_{\mathit{ij}}-{\epsilon }_{\mathit{ij}}^n<0\end{array}\right)$$ 9

“ − ” is the opposite correlation, and “ + ” is a clear correlation. A gray relation’s strength may be measured by assigning stages to such signals. Accordingly, JCL and the JGI scales explain the data, with probably 2 different links being described.

To indicate the variables of t and the rising outcome variable, X_tm is set. The high values from phase t to phase t − 1 and the lagged ordering of the increasing variable, q, are also shown. Frequency distribution-mixed frequencies regression has the following particular form:

$$t=\alpha +\left(0,t,m,+,1,t,-,m,m,+,\cdots ,+,m,t,-,1,m,+,\cdots ,+,2,m,t,-,2,m,+,\cdots ,+,q,m,t,-,q,m\right)+t$$ 10

Abbreviated as follows: = α + (θ,)tm + t

$$mtm=t-i/mm$$

The delayed operator L goes by the letter L. the classic regression model’s attributes and t = N (0, 2) are similar to the randomized perturbations period’s characteristics.

A vector autoregressive lagged-mixed statistics regression model is proposed by (2013) in mixed statistics (ADL-MIDAS model). Given that the model’s components are of the first order, we may write down the model as follows: t + 1L = α + γtL + (m,)H + tL.

According to this study, China’s petroleum safety ratings are Yt + 1L. Rising explaining variables are measured by XtH and reduced independent variable by m, with the latter denoting the quantity of data sample from t to t + 1. Here, monthly GPRs are (m,) = i = 1(i;)i − 1)m.

It is a phrase that represents a random error. Lagged operators are used in conjunction with a quadratic weighting factor for rising data to create the weight function W(L1m,θ) = ∑i = 1pxω(i;θ)L(i − 1)/m. The rising explanatory variable’s distributed lagged ordering is denoted by the symbol px.

In their paper, a wide range of MIDAS models and weights factors are presented by Nasim and Fatima (2020). Almon polynomials scale value, exponential Almon polynomials value component, beta weight function, etc., are some of the most commonly used weighted values. This study uses the beta function as a weighting factor because of its usefulness to techniques used to collect and the accessibility of its variables. The MIDAS model uses beta sources. List probability density. The beta distribution family has various forms that just 2 factors may represent. The overgrowth of parameters produced by combining data may be efficiently avoided. This study uses the beta functional as the weighting factor because of its evaluation and accessibility of variables. The MIDAS model uses beta sources. List probability density. The many variants of the beta distribution family may be represented using only two variables. When data is mixed together, the multiplication of parameters may be avoided. It may be described in terms of what it does: (8)ω_i(θ) = ω_i(θ1,θ2) = f(x_i,θ1,θ2)∑i = 1imaxf(x_i,θ1,θ2).

ω_i(θ) = ω_i(θ1,θ2) = f(x_i,θ1,θ2)i = 1imaxf(x_i,θ1,θ2), where i is the distribution lag order, Imax is the maximum lag order of the weight function. The variation range of i is i = 0, 1, …, Imax. In addition, x_i = i/Imax, where i is the distribution lag order, and Imax is the maximum lag order of the weight function. The variation range of i is i = 0, 1, …, Imax. In addition, x_i = i/imax, f(x_i,θ1,θ2) = x_iθ1 − 1(1 − x_i)θ2 − 1Γ(θ1 + θ2)Γ(θ1)Γ(θ2), Γ(θ) = ∫0∞e – xxθ − 1dx.

$$f\left(x_i,,,\theta ,1,,,\theta ,2\right)=xi\theta 1-1\left(1,-,x,i\right)\theta 2-1\mathrm{\Gamma }\left(\theta ,1,+,\theta ,2\right)\mathrm{\Gamma }\left(\theta ,1\right)\mathrm{\Gamma }\left(\theta ,2\right),\mathrm{\Gamma }\left(\theta \right)=0\infty e-xx\theta -1dx.$$

Data

In this study, we use the GPR index introduced by Caldara and Hosseini et al. (2013) to quantify geopolitical unpredictability. Terrorist threats, nuclear tensions, war threats, and military battles between nations all have a role in this. Through a series of textual searches, have tallied the total number of times that references to geopolitical tensions have been made. Eleven domestic and foreign newspapers’ digital archives were combed, and the GPR of 19 areas was calculated (Gielen et al. 2019). Compared to other methods of assessment, GPR’s readings are quick and frequent. We analyzed the potential effects of global GPR, GPR BROAD from oil-exporting countries, GPR from oil-importing countries, and China’s GPR on China’s oil security. India, Nepal, Bhutan, Sri Lanka, Afghanistan, and Pakistan are among the nations that export oil to the extent that this information is currently available. Other than the aforementioned five nations, there are a total of 14 nations and areas that do not participate in oil exports.

This study covers the period from January 1999 to December 2017 since there is a dearth of incomplete data from the last few years. The National Bureau of Statistics provides the figures for reliance on oil imports. Both the CEIC financial database and the U.S. Energy Data Management are mined for the oil carbon intensity and the oil carbon dioxide emission percentage (EIA) (Lin and Zhu 2019). Brent crude oil price from the BP Statistical Review of World Energy is used as the international crude oil price and U.S. Energy Information Administration figures for China’s GDP and population. The author uses data from BP’s Statistical Review of World Energy to compile the rest of the measures (Ahmad et al. 2020). Some examples include the percentage of oil imported as a whole, the part of global oil manufacturing that China holds, the reserve manufacturing ratio, etc.

Results and discussion

Gold scarcity helps stimulate financial development in developing nations. The health of a corporation may be affected by gold in various ways. Money serves as a store of value and a medium of trade; hence it must be protected by money. Furthermore, it is a secure kind of finance, among other benefits. Gold’s value is set on the global market, where it is traded at a constant rate. Gold was a safe refuge for stockholders during the great depression (Bucelli et al. 2018). According to the economic model, when times are tough, share prices fall and gold prices go up. This is why gold is seen as a haven for investors concerned about inflation. The share market often experiences large-scale crashes. Much research has shown that bond prices drop and gold prices rise during times of crisis. The glue is high; therefore, it is a good time to sell gold and purchase stocks.

Political risk index score

Oil is a crucial part of today’s global economy and geopolitical risk (GRP). The development and improvement of transportation have contributed to a rise in oil usage (Dotan et al. 2022). Oil is essential to the running of many sectors, and even a little fluctuation in oil prices might affect share industry results. The economic and financial development systems of the nations that import and export oil determine the degree of this volatility. Table 1 demonstrates that oil price and gold price futures have the largest mean values for liquidities, indicating that these two futures are the least liquid. A large percentage of trading days with no training is a sign of illiquidity, and it is also the cause of the low median value for research estimations of stock price movements in sample share prices. It has been shown via study that size bias may be influenced by differences in mean and standard liquidity variations across product future industries (Xu et al. 2020). To account for any size bias in the liquidity assessments, this research normalized the data. Five common measures of liquidity are summarized statistically in Table 1. These results demonstrate that the scale effect has been mitigated in commodities’ future markets generally. Livestock futures have a low average value because of the frequent occurrence of trading days with no buyers or sellers (Tsujimoto et al. 2019).

Table 1.

Descriptive statistics

	LGE	LGB	GPR	GPR BROAD	GPR NARROW	GPR THREAT	GRP ACT	Oil
Mean	5.7768	4.9887	4.8665	4.6988	4.90898	4.9487	4.099	3.007
Median	5.2887	4.9087	4.8765	4.6687	4.85787	4.8897	4.0899	3.02543
Maximum	6.8698	5.0677	5.98876	5.6898	5.99687	6.0477	5.697	5.0321
Minimum	4.3487	4.8088	4.0398	4.1887	4.04588	4.0587	2.5299	2.5678
Std. dev	0.6077	0.0676	0.3498	0.2786	0.36287	0.3698	0.5198	0.8756
Skewness	0.9098	0.5687	0.4288	0.6198	0.39487	0.4198	− 0.1980	− 0.1660
Kurtosis	3.1398	2.5087	2.7187	3.2698	2.61676	2.5777	3.3898	2.8976
Jarque–Bera	254.6888	116.6198	61.0878	121.1879	59.9987	67.0098	18.4798	23.7654
Probability	0.2222	0.2222	0.2222	0.2222	0.222222	0.3345	0.4432	0.4321
Observations	1860	1860	1860	1860	1860	1860	1860	1860

Findings suggest that effects on futures market sizes may be moderate and that product future markets exhibit wide variation in standard deviations of liquidity indicators. To reduce the potential for size bias, we recalculated the liquidity indicators (Zhang et al. 2018). The size effect on product futures industries is reduced by looking at Table 1, which offers a statistical summary of five basic liquidity parameters (Table 1).

As stand-ins for the global oil price, we choose the price of crude oil, often known as petroleum, including GRP, LGE, LGB crude oil, Brent crude oil, gas oil, and heating oil. Modern economic systems see these crude oils as important production factors, which means they may have far-reaching consequences for several sectors of the international economy (Barrutia and Echebarria 2021). In addition, the prices of geopolitical risk (GRP) crude oil, Brent crude oil, gasoline, and heating oil are widely recognized as major worldwide benchmarks for petroleum prices. For example, GRP crude oil is produced and sold all over the globe as a reflection of the global supply and demand framework, as described by Cross and Todorov (2014). However, the relevance of studying crude oil’s directional predictability to share prices may be highlighted by the fact that its price has fluctuated widely over the last two decades owing to economic crises, political tensions, conflicts, global wars, etc.

The accessibility of information for the relevant factors dictates both the start and end points of the sample periods. DataStream provides information on oil prices and financial markets, whereas the website http://www.policyuncertainty.com/about.html is where you may get information about GPR and uncertainty. After that, we use the standard approach to compute daily returns by deducting the natural logarithm of prices on day t − 1 from prices on day t.

The GPR index is constructed by Kaklauskas et al. (2018).

The index is calculated by counting how many times key phrases associated with GPR appear in each of the selected newspapers every day starting in 1984. And last, they standardize the index to a value of 100 for the years 2005–2020.

Energy independence, energy-saving, carbon dioxide emission, technical advancements, and refinery systems analysis are vital topics of existing research into China’s refinery sector in China’s petroleum industry. Walls examined China’s oil refinery business in the context of the international economy using statistics since 2008 (Table 2) but was upbeat about refinery capacity increase in the upcoming (Ranjan et al. 2021) from the standpoint of energy supplies. China’s oil industry chain was analyzed to see how stock market returns reacted to worldwide oil price changes. Impacts on oil production, need, demand, shocks, spillover effects, and cautious requirement shocks significantly impacted current and foreseeable oil availability. In their paper, Mitchell and Mitchell explained the structural crisis in the oil and gas sector throughout the globe. According to the study, the worldwide oil sector is shaped by the management and goals established by national oil firms like China. The country’s enormous oil companies and the authorities that control them must adapt to a new context that has decreased oil consumption. Still, domestic demand has grown excessively due to pricing mismatches between local and overseas markets.

Table 2.

Political risk index score

Country	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Afghanistan	0.41	0.47	0.71	0.71	0.66	0.57	0.77	0.59	0.46	0.49	0.64	0.59	0.47	0.62	0.65	0.64
Bangladesh	0.47	0.38	0.46	0.52	0.58	0.61	0.67	0.69	0.57	0.41	0.69	0.76	0.55	0.54	0.66	0.56
Bhutan	0.59	0.68	0.77	0.76	0.68	0.67	0.65	0.69	0.71	0.69	0.77	0.77	0.71	0.84	0.85	0.65
India	0.57	0.64	0.46	0.48	0.51	0.53	0.49	0.47	0.48	0.36	0.45	0.34	0.37	0.39	0.43	0.53
Nepal	0.63	0.69	0.69	0.62	0.59	0.74	0.76	0.79	0.75	0.81	0.83	0.65	0.67	0.79	0.81	0.68
Pakistan	0.37	0.45	0.41	0.39	0.38	0.37	0.41	0.42	0.34	0.31	0.32	0.32	0.34	0.37	0.28	0.33
Sri Lanka	0.42	0.47	0.47	0.41	0.39	0.42	0.41	0.44	0.29	0.37	0.32	0.34	0.36	0.29	0.44	0.42
Maldives	0.64	0.84	0.63	0.61	0.59	0.72	0.73	0.76	0.75	0.81	0.79	0.67	0.68	0.75	0.61	0.59

Geopolitical risk index score

The Chinese government needed a solution to the issue and has endeavored to promote oil sector innovations. China’s energy supply networks were examined by Leung et al. When it comes to the security of the country’s electricity generation networks, China’s reliance on the refining oil products of the USA is highlighted. Montero et al. (2012) provided an overview of China’s petroleum company’s present growth stage from the perspectives of oil supply reliance and global commerce (Table 3). They reviewed the current dangers of energy flow, finances, and the climate using design options from supplier viewpoints. The threat of refiner equipment breakdown is a concern. Because of the large amount of Middle Eastern oil with a rising sulfur content and the rising need for reduced transportation fuel, many new refineries are needed to perform oil with rising sulfur content and start producing softer and smoother crude oil. According to the researcher, this would cause much more excessive (Table 3).

Table 3.

Physical oil supply risk

Country	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Afghanistan	0.38	0.51	0.69	0.66	0.70	0.61	0.80	0.61	0.50	0.51	0.70	0.61	0.51	0.59	0.71	0.64
Bangladesh	0.51	0.41	0.50	0.48	0.61	0.59	0.71	0.71	0.61	0.39	0.71	0.80	0.61	0.60	0.70	0.56
Bhutan	0.61	0.70	0.80	0.80	0.70	0.71	0.71	0.71	0.66	0.71	0.80	0.80	0.69	0.90	0.91	0.65
India	0.61	0.70	0.50	0.50	0.49	0.49	0.51	0.51	0.50	0.41	0.51	0.40	0.41	0.41	0.50	0.53
Nepal	0.59	0.71	0.71	0.59	0.61	0.80	0.80	0.81	0.81	0.77	0.77	0.71	0.71	0.81	0.77	0.68
Pakistan	0.41	0.51	0.39	0.41	0.40	0.41	0.39	0.39	0.41	0.29	0.29	0.29	0.41	0.41	0.31	0.33
Sri Lanka	0.42	0.47	0.47	0.41	0.39	0.42	0.41	0.44	0.29	0.37	0.32	0.34	0.36	0.29	0.44	0.42
Maldives	0.64	0.84	0.63	0.61	0.59	0.72	0.73	0.76	0.75	0.81	0.79	0.67	0.68	0.75	0.61	0.59

In addition, several studies examine the refinery company’s fuel efficiency and emissions that impact the environment. There are several ways to reduce the amount of energy used in refinery and converter operations, and. assessed the potential for power in technology advancement. An examination of the reduction potential suggested by various technical advances was conducted by Gamage et al. (2021) to examine CO₂ emission in Chinese chemical industrial processes.

Expensive-to-run fuel performance and system administration are some of the focus areas of several studies. Inter MPEC planning model developed by Mandema et al. (2005) was used to optimize the planning of the fueling gas line in refineries (see Table 3). The diesel fuel system of the refinery may benefit from the suggested method’s increased energy efficiency (Cockerill et al. 2004). Refinery sector heat recovery was the primary focus of. Modeling and simulating an ORC system to collect inter-wasted heating elements included considering thermal efficiency, platform simplicity, technical feasibility, and economic aspects. Installation growth is coupled with an issue with capacity. The conducting site between power businesses and grid firms was addressed, and optimum simulations were constructed. According to Martinez-Tejada et al. (2021), China suffers from a lack of traditional thinking. It was found that China’s power source was formed by resource scarcity and capability under-utilization. Below was driven by policies and long-term investment choices related to fuel rising prices ingraining. To summarize, the refinery company’s excess supply has been noted just a few times in the previous study. However, existing research suggests that China’s refining industry faces a severe but unsolved overcapacity problem because of the supply–demand conflict in the national oil sector, the processing facility electricity security concerns, and the immediate requirement for developed factories to fulfill tighter pollution rules.

Market liquidity is a significant factor in the oil vulnerability index as a whole. India, South Korea, and Japan have the worst situations, with values of 0.168, 0.175, 0.08, and 0.68, correspondingly. In contrast, a stronger indicator of market liquidity is the fact that Afghanistan, Bhutan, Nepal, and Bangladesh all have values of about 0.001. It is easier for nations like Afghanistan, Pakistan, Nepal, Bhutan, and Bangladesh to move between oil suppliers because of their lower market liquidity. China, the USA, India, South Korea, and Japan all have large impacts on market liquidity on total risk, whereas Afghanistan, Pakistan, Nepal, Bhutan, and Bangladesh all have small effects (Ma et al. 2021). Table 3 displays the totals for those nations most at risk due to their dependence on oil. Pakistan, Sri Lanka, Nepal, Bhutan, Bangladesh, and Afghanistan all had scores higher than 1, making them susceptible. Countries with scores below 1 but over 0.50 are less at risk.

Dependence risk

As a result of recent research, it has emerged that examining power issues from a supply chain viewpoint has several benefits than overanalyzing them from a more traditional approach. When it comes to brand distribution, the supply chain involves everything from purchasing raw materials to converting them into finished goods. Supply chain management strategy and optimization are the primary focus of current research on the supply chain.

The planning process, yearly review, and optimization of the oil production chain are the primary research areas. Iversen et al. (2002) have reviewed China’s oil production chain safety from a national viewpoint. According to them, the supplier is still in its adolescence in the oil business. Empirical research by Zhang et al. (2021) examined the UK’s adoption and effectiveness of sustainable oil and gas production chains. There are possible improvements for oil supply security focused on supply chain analysis, and a DEA-like model was constructed to assess oil supply security. A study by. examined the effects of several calculated decision options on inventories reducing defects to minimize inventories costs and maintain the same overall risk for stocks from supply chain management practices. According to Duan et al. (2010), a practical technique was used to study supply chain efficiency and cluster competitiveness. The oil production chain of a particular oil firm was modeled using linear models offered by Can and Canöz (2021) who used a disconnected and integrated approach and gave optimal planning for the company. Silva and Henriques (2021) used a Lagrangean decomposition technique for investment planning in the oil supply chain under uncertainty and risk concerns. Vos and Cattaneo (2021) used a scorecard to assess supply chain management’s success. Excess capacity in the supply network, on the other hand, is rarely discussed (see Table 4).

Table 4.

The results of the unit root test

Variable	ADF test	PP test
	Trend and intercept	Trend and intercept
LGE	− 62.776***	− 85.7760***
LGB	− 86.79987***	− 87.00982***
GPR	− 58.3998**	− 123.6974***
GPR BROAD	− 23.0881***	− 93.1998***
GPR NARROW	− 30.2798***	− 92.0499***
GPR THREAT	− 17.2984***	− 95.9986***
GPR ACT	− 8.8488***	− 100.6678***
OIL	− 11.6875***	− 104.2998***

It is well known that most socioeconomic time series data suffer from the unit root issue. Before doing any kind of estimate (LGE, LGB, GPR), we checked to see whether our data had a unit root. Selecting the right time series model for our data is crucial for avoiding the estimate of false regression. We conducted (Ma et al. 2022) four-unit root tests as a result, including the two gold standards (the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP)) and two more advanced types (with structural breakdowns) (ADF and PP). The data is shown in Table 4. This proves that the variables in this study are stationary at the level of the data and the first difference. As a result, the autoregressive distributed lag (ARDL) model is the most appropriate for determining the parameters of our model.

Physical oil supply risk

A distribution network assessment investigates oil companies' excess capacity and electricity safety concerns. When it comes to supply chain analysis, it provides a holistic overview of the entire industry while also considering particular sectors and their interactions with their transmission and distribution sectors. In the refinery sector, operating capability and excess capacity are governed by supply from the transmission and distribution markets. It also relies on the hazards likely to be faced at a particular time. As a result, from a systemic viewpoint, supply chain analysis may highlight the driving causes behind the overcapacity issue. Supply-chain-related factors, such as refined national products, inventory control, and transportation ability, affect the oil industry’s ability to deal with disruptions in crude oil imports. As a result, distribution network analyses on power security challenges can present sector responses from dynamic response viewpoints (Table 5).

Table 5.

PCA: the correlation matrix between different geopolitical risk indicators

	GPRI	GTT	UCR	URT	SAU	VC
LGE	2	0.51886	0.84776	0.56777	0.6288	0.66423
LGB		2	0.72498	0.55098	0.36991	0.58849
GPR			2	0.20388	0.24668	0.48842
GPR BROAD				2	0.21882	0.6664
GPR NARROW					2	0.27699
GPR THREAT						2

Market risk index score

It has been frequently used in studies on capital investment mechanisms, improved performance, and policy studies in the energy sector. When it comes to China’s PV power advancement, for example, created a dynamic model that included financial and strategic factors. In their different scenarios of nontraditional oil production, used a system dynamic method. Chen et al. (2021) built a system dynamics simulation model to understand better Canada’s power supply and demand. This section introduces a simple foundation for a dynamic response modeling China’s oil production network. Supply chains are made up of upstream, midstream, and downstream. Drill stations, upgrade systems, and the main power plant are parts of the upstream industry. Refinery and chemical factories make up the midstream industry. The lower industries include transportation and petroleum.

Infrastructure risk

Petroleum is supplied by imports and domestically produced reserves. The term “nationwide petroleum products containers” relates to both government-owned corporate strategy batteries and advertising collections possessed by oil corporations. Then, oil is transferred to production sites following demand arrangements made by the oil corporations. Using a production schedule, petroleum is refined into oil products such as gasoline and diesel. Most of the time, supply and production plans are set at the start of the year and then tweaked each month to reflect market conditions. It is standard procedure to transport petroleum products to local storage and then distribute them to small shops. Purchasing and use of oil products follow. Crude oil is also imported and exported for local storing.

Each thirty period, the everyday processing of petroleum products and the output rates of fuel, gas, and petroleum are modified in our models. The production adjustment mechanism is a crucial refinery industry function. Suppose crude oil supply and refineries are adequate. In that case, our model predicts that the daily production of oil products will match or surpass the preceding 30 time-steps average daily consumption but will not exceed the limits of possible output. There is also a limit on the proportion of fuel in diesel and petrol engines between 2 and 4 (Yumei et al. 2021).

Gasoline and petroleum consumption services, on the other hand, are provided on a daily and monthly basis. The standard deviation for gasoline usage spans 15% compared (February 2008) to 13% (May 2003), and 93 percent of the prediction errors fall within 8 to 8%. More than 81% of the relative errors in kerosene consumption are within the limit of 15 percentage points to 15%, while kerosene demand has a close GPR, LGE, and LGB error range of 49.9 to 23.66%. Equations (1), (2), and (3) have fitting values shown in Table 1. The corresponding default values in Table 2 are offered in different colors, which is also reported in Table 6. The colors red and blue denote positive and negative mistakes, respectively. Using a dark blank indicates a higher absolute inaccuracy. In the first two years, 2008 and 2011, exports of oil products included significant mistakes.

Table 6.

Regression analysis of oil supply risk

Variables	OLS-random effect	OLS-fixed effect
LGE	− 0.9691	3.4371
	(− 0.4459)	(− 1.199)
LGB	− 0.7021*	− 4.8561*
	(− 2.81111)	(− 3.6191)
GPR	7.1778*	1.0561*
	(− 2.1551)	(− 19.6041)
GPR BROAD	4.7114*	1.0241*
	(− 2.1739)	(− 39.4121)
GPR NARROW	0.0788	0.0151
GPR THRAT	− 0.9691	3.4371
	(− 0.4459)	(− 1.199)
GPR ACT	− 0.8891	2.5071
	(− 0.4519)	(− 1.219)
OIL	− 0.9688	4.4411
	(− 0.4461)	(− 1.188)
Constant	− 0.7021*	− 4.8561*
	(− 2.81111)	(− 3.6191)
R2	7.1778*	1.0561*
	(− 2.1551)	(− 19.6041)
N	4.7114*	1.0241*
	(− 2.1739)	(− 39.4121)
Adj. R2	0.0788	0.0151

Predicted everyday petroleum, fuel, and kerosene consumption are fed into the model using IAEA’s World Energy Outlook 2013 scenario study findings (IEA). Choice functions inside the fuel supply system are activated by inputs from downstream to upstream industries, resulting in transport and distribution plan changes. Fuels such as petrol, diesel, and kerosene are made from crude oil at refineries. Upstream supplies, refineries, and lower needs influence the amount of processing crude oil. The division in the upstream side and only petroleum, diesel, and kerosene are transported since statistics on other oil products are missing.

Every moment in the system corresponds to a single day of actual time. Every month is divided into thirty similar days for ease of computation. Each step of the design mimics the usage, manufacturing, transportation, and storing of oil products.

Overall composite index score

Refining reserve is used for a short period before being transferred to local storing. Local stores handle imports, exports, and transmission to local downstream shops. The collection management strategy is the primary function in the accumulated sector. Shorter and higher limits are imposed to minimize supply problems or overstocks, as in the crude oil supply industry. Based on the average flow-ins and stream of the previous 365 periods, we change the top limit of local storing every 365 moments. A safety factor and the maximum level are assumed to be multiplied together to get the lower threshold of the range. Petrol components have different safety coefficients. If the actual capacity falls below the minimum limitation at any point in the modeling time, order indications will be transmitted upstream. The extra storage space will be transferred to lower-priority areas if demand surpasses the max limit.

Trump’s election as president of China in 2016 was widely anticipated, but his administration has introduced new levels of uncertainty to global geopolitical threats, notably those involving China. Therefore, the paper treats Trump’s election as president of China in 2016 as an exogenous event and treats it as the instrumental variable of China’s geopolitical risk (Barykin et al. 2020). Column (1) of Table 6 is the result of first-stage regression, which has a positive relationship with the geopolitical risk of China (GPR) at the 1 percentage significance level, which supports the nature of relevance for good instrument variable regression that is shown in columns (2) and (3). Consistent with H1, the data demonstrate that the geopolitical risk of China (GPR) is positively associated with both the. We performed a battery of experiments to confirm the usefulness of instrumental variables. The Anderson test has a p-value of 0.000, indicating that there is no under-identification problem; the, which is greater than the critical discriminant value of 16.380, indicating that there is no weak identification problem; and the Sargan test has a p-value of 0.000, indicating that the equation.

Meanwhile, prior work (Williams 2021) suggests that we may employ the geopolitical risk uncertainty of countries other than China as an instrumental variable (Yin et al. 2021). First-stage regression results shown in Table 6 show that it is positively related to China’s geopolitical risk (GPR) at the 1 percentage significance level,

Robustness analysis

Extraction and purification manufacturing excess supply in China is discussed by first defining the supply abilities of the oil production sequence as follows: The retrieval, optimization, transportation, collection, and structures differ of oil work together to match the last requirement and avert supply problems inside specific periods under certain levels of customers’ feedback. If any part of the oil production chain fails, it might lead to an oil production deficit. It is reasonable to anticipate that the oil production chain’s production and transportation processes will be unaffected for a while. Refinery and storing administration significantly impact the oil-supplied company’s capacity to meet demand. For this section, statistical data from 2013 is used. Oil production capabilities in coping with diverse demand fluctuations are studied, but the over degree of the refinery business is quantified based on these results.

providing support for the nature of relevance for a good equipment variable; in contrast, satisfying the nature of heterogeneity for a good instrument factor. The regression analysis is shown in columns (4) and (5). Consistent with H1, the findings indicate a positive relationship between China’s geopolitical risk (GPR). We conducted a battery of tests to confirm the usefulness of instrumental variables and found that the p-value of the Anderson test is 0.000, indicating that the IV2-Korea has no under-identification problem.

Discussions

The COVID-19 problem has had a far-reaching effect on energy consumption, and the steps used to slow it have not been seen for 70 years. The complete impact of the current scenario is yet unclear, but it will be determined by the length of the recovery pathways and lockdown measures adopted throughout the globe (Guisado Hernández et al. 2021). This unanticipated circumstance, together with the state stimulus packages that will be implemented over the following several years, will have far-reaching consequences for the energy sector and the clean energy and energy security transitions that are now underway. Across the value chains of the biomass energy business, the financial effect is felt, with most energy firms reporting (Jaisinghani and Kanjilal 2019) significant revenue losses. Less demand for their goods, such as gas, coal, oil, and power, and reduced pricing, hurt them. As a result of a lack of available storage space, oil prices fell dramatically on average, with geopolitical risk reaching historically low negative prices.

In the aftermath of the COVID-19 pandemic, the biomass sector may develop in quite different ways than in the past. Weak energy companies in any industry may be weakened by low demand and prices, which can put pressure on the company’s finances. Some company segments, such as those with renewable power projects in the strongest economic condition, will be buffered from market signals. These market shocks will be felt most keenly by privately held businesses with extensive price sensitivity. There will be a squeezing together of sellers and buyers.

Concerns concerning energy security have been raised because investments will be required even if it takes a considerable amount of time for global energy consumption to recover to its trend before the COVID-19 crisis. Keeping palm oil output at current levels, reinvesting in aging electricity networks, and replacing aging power generating capacity with a capital-intensive mix of flexible sources and renewables all contribute to maintaining the current levels of energy supply. Expenditure in such endeavors will not be bolstered even by a moderate economic recovery.

Due to worldwide supply chain disruptions, biomass renewable energy solutions are in limited supply. Therefore, the biomass sector needs to prioritize the domestic production of renewable energy technology. Because of this whole reliance on foreign sources for technological advancement, the danger is quite high. Therefore, various methods and procedures for enhancing production capacity should be investigated. Several factors have contributed to a slow decline in renewable energy generation in several countries’ uncertainty about renewable energy’s true capacity, a lack of a trained labor force and financial resources, and an absence of adequate R&D initiatives. As a consequence, in the epidemic condition that affects the unsustainable enterprises active in the off-grid energy sector, the poor facilitation of energy access is a direct result of the lack of local capabilities and disruption in the biomass energy technology supply chains. These factors need cutting-edge practices and efficient policies from biomass suppliers and other interested parties to sustain regional manufacturing capabilities. To achieve development goals related to the sustainability pillars, it is necessary to create and improve their energy technologies and to ease energy access (economic, social, and environmental).

Production sectors, renewable energy-based power generation, economic incentives for R&D projects, carbon trading/pricing, the adoption of Feed-in Tariffs, and other associated regulations might all play a role in fostering the production of biomass renewable energy technology (Muggeridge et al. 2014). It is obvious that a wide variety of incentives already exists; nonetheless, it is important to give top priority to those that might have a significant impact on the decentralization of biomass renewable energy technologies. Investments in research and development (R&D) may be eligible for tax breaks and subsidies, as well as repayment-free cash prizes from a variety of sources. Some additional steps should be considered as well, such as reducing the income tax for selling electricity generated by locally created renewable energy technology, providing economic (Vakulchuk et al. 2020) subsidies to power generated using locally made renewable energy and green technology innovation, etc.

Conclusion and policy implications

An empirical examination of Southeast Asian nations’ oil supply vulnerabilities was carried out in this research. Using a variety of variables, such as supply chain operations, infrastructural risk, market volatility, transportation risk, and dependency risk, a conceptual foundation that is rather thorough is constructed. These indications have been combined using a mathematical composite indicator. Each signal’s weight has been limited to provide a valid assessment. The evaluation has also included country risk for oil-importing and exporting nations. According to our research, the danger potential of these nations varies greatly. For example, financial, social, and geostrategic challenges are all possible in Bhutan and Afghanistan. Maldives, Nepal, and Sri Lanka have the lowest supply risk score, which indicates that they are more likely to shift their oil suppliers.

To attain international oil security, international and domestic energy policy must take into account gasoline consumption sustainably and oil supply. Because each nation has a unique risk profile owing to its signals, each country needs a unique policy instrument to lower its oil supply risk. To achieve the goal, national oil supply security policies should be given the highest priority. The following are the ramifications for public policy that we foresee. Switching to renewable, optimizing energy structure, conserving resources, using and expanding clean energy, and reducing oil consumption are all important first steps in reducing the danger of oil imports from outside. To lessen their dependency on imported oil, oil-importing nations should encourage FDI in local manufacturing and research. Foreign investment in less-developed nations should be protected to guarantee a steady rise in oil imports. Countries that rely on oil imports should work together with other oil importers to reduce their dependence on oil imports. To maintain a steady supply of oil, it is necessary to support a wide range of oil import sources. Exporting nations may shift their reliance on oil from high-risk areas like Africa and Latin America to politically stable ones like Russia, Canada, and South America. This would reduce and diversify their reliance on these regions. Lastly, governments need to maintain the safety and security of transportation by diversifying their transportation routes. The TAPI project, which would deliver Caspian Sea natural gas from Kyrgyzstan through Afghanistan and Pakistan to Delhi, should be pushed to reduce maritime dependence on oil importation. The scarcity of oil resources and the advancement of technologies should be done in order to minimize the effect of an oil supply disruption.

Author contribution

Zhenxing Li: Conceptualization, Software, and Data Creation, Revision of manuscript.

Mohammad Maruf Hasan: Conceptualization, Methodology, Software, Data Creation, Writing-original draft preparation and Revision of manuscript.

Zheng Lu: visualization, editing, proofreading, and Revision of manuscript.

Data availability

The data that support the findings of this study are openly available upon request.

Declarations

Ethical approval and consent to participate

The authors declared that they have no known competing financial interests or personal relationships, which seem to affect the work reported in this article. We declare that we have no human participants, human data or human issues.

Consent for publication

We do not have any individual person’s data in any form.

Competing interests

The authors declare no competing interests.

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