How do climatic factors affect gas prices volatility in Europe?
Title “Gas price volatility in Europe in the last 3 years”
Multiple regression model
Research Gap:
How do climatic factors affect gas prices volatility in Europe?
How have technological advancements affected the cost of gas extraction, and what are the implications for gas prices/ long-term pricing trends?
What role does the development and utilization of LNG technologies play in shaping gas prices in Europe?
Which roles do geopolitical factors, such as the conflict between Russia and Ukraine, supply disruptions, COVID-19 pandemic and European policies play in determining gas price in Europe?
Chapter 3 Research Methodology (Typically, approx. 1000 to 1500 words).
The Research Methodology commences by restating research objectives and details the precise research questions of the project. Your methodology is assessed on whether it is a valid approach to answering these questions. Students should identify the choice of philosophy and method, the reasons why that choice was made and the pros and cons of that choice. Justification should be based on the research question and context and not primarily on concerns relating to time and cost. A good methodology will argue for and justify each decision that is taken when arriving at the research design. The chapter should contain information on;
the limitations of secondary research (the gaps identified in the literature review),
the research philosophy
the research strategy (qualitative or quantitative methods)
detailed discussion of data collection instrument (interview topic guide, survey etc.)
population and sampling approach
research instrument administration (how you collected the data)
data inputting
data analysis plan
ethical issues that were considered and how addressed what constitutes the sample
the sampling criteria used and
the response rate
The conclusion of this chapter should provide a summary of the main points that have been covered. The conclusion should also direct the reader as to how the contents of this chapter link in with the contents of the next chapter, your findings.
Chapter 4 Findings/Data Analysis (Typically, approx. 2500 to 4000 words)
The chapter should present the primary data you have gathered. You should describe the nature of the sample and the defining characteristics which make the participants of interest to your study.
After describing the sample, you should proceed to analyze your data so that you clearly show how it relates to your research questions/ hypotheses.
You need to present your results in an accessible manner to show what the research has uncovered and to include only the most pertinent statistics or interview quotes as core evidence.
For quantitative data, summary statistics should be used i.e. means, frequencies. Statistics should be presented to no more than two decimal points to aid understanding. You should convert SPSS output to excel tables and give full indication of questionnaire items and not abbreviated labels.
Chapter 5 Discussion (Typically, approx. 1000 to 1500 Words)
In the chapter introduction restate the research aim, objectives and questions. In this chapter you relate the findings to each question and should highlight the most pertinent and important results.
The next step is to critically discuss the findings with reference to the wider research literature. What this means is looking at where your findings are consistent with theory and where they contradict theory.
This is the heart of the dissertation and must be more than descriptive. This chapter develops analytical and critical thinking on results and analysis with reference to theoretical arguments grounded in the literature review.
Chapter 6 Conclusions (Typically, approx. 1000 to 2000 Words)
In this chapter you summarize what has been learnt from the whole process, noting the specific contributions of your work to knowledge and practice. This will include the research gaps, the methods used for data collection and the findings.
After you have summarized the nature and value of your dissertation content, you should conclude by discussing the implications for two key audiences:
(1) Practitioner (e.g. how will your insights affect their understanding; how might practitioners utilise this understanding based upon your evidence?
(2) Academic ( how will your insights affect understanding of your subject area (e.g. topic; theories; concepts; etc.) and how might academic audiences utilize this insight based on your evidence?
You should discuss the limitations of your research and make suggestions of how further research might address these limitations – be mindful that you remain consistent with your chosen research philosophy. Remember that limitations aren’t always problems! Rather, they set the parameters for understanding the value of your work. You should also consider how research in this subject area could be pursued in the future – this will demonstrate your understanding of how your study contributes to the development of the overall body of knowledge.
COVID-19 Global Impact: A Visual Analysis
Introduction
Millions of lives have been impacted, and economies all around the world have been affected by the COVID-19 epidemic. By looking at significant data on the virus’s distribution and effects in many nations, this research seeks to examine the COVID-19’s worldwide impact.
I have gathered a complete dataset for this investigation, which includes COVID-19 statistics from several nations. The dataset includes statistics on the population, total cases, total fatalities, total recoveries, and other pertinent variables. Accuracy and dependability are ensured by the constant collection and updating of the data from reliable sources.
Tableau offers a variety of visualization techniques aimed at efficiently presenting the data. To illustrate various aspects of the COVID-19 epidemic, I will use a variety of chart formats, including bar charts, pie charts, line charts, scatter plots, geographic maps, and stacked area charts. Each visualization will highlight a different aspect of the data, allowing us to look for trends, patterns, and connections.
To clearly grasp the relative impact of the virus, bar charts will be utilized to compare the total cases, total fatalities, and total recovered for each nation. Pie charts will illustrate geographical inequalities by visualizing the distribution of all cases by continent. Line charts will show the development of all cases over time for each country, enabling us to examine the growth rate and pinpoint key moments. To better understand the severity of the virus globally, scatter plots will be used to examine the association between total cases per 1 million people and total fatalities per 1 million people. Geographic maps will give a global perspective by visualizing the total cases or total fatalities per nation. Stacked area charts will show the distribution of new cases, cases that have been recovered, and fatalities across time, illuminating the pandemic’s shifting dynamics.
Literature review
The COVID-19 epidemic has arisen as one of the most severe worldwide health catastrophes in recent memory, needing careful investigation to comprehend its effects on different facets of society. With an emphasis on important statistical analysis and data visualization strategies, this literature review seeks to offer a thorough overview of the research that has already been done on the COVID-19’s worldwide effect.
Global distribution and Epidemiological Analysis: Several research have looked at the epidemiological features of COVID-19 and its global distribution. For instance, Wu et al.’s (2020) research brought attention to the virus’s quick spread and the necessity of putting strict control measures in place to slow it down. Additionally, Li et al.’s study from 2021 stressed how important it is to comprehend the fundamental reproductive number (R0) in order to gauge the severity and possible effects of the virus in various areas.
Impact on Healthcare Systems and Public Health: COVID-19’s effects on healthcare systems and the general public’s health have been thoroughly investigated. The burden on the healthcare infrastructure was highlighted in research by Goyal et al. (2020), which emphasized the necessity for resource allocation and capacity planning. In addition, research by Remuzzi and Remuzzi (2020) emphasized the significance of putting in place public health measures including testing, contact tracking, and vaccination programs to restrict the virus’s spread.
Economic Effects and Socioeconomic Impacts: The epidemic has had significant global economic effects. Comprehensive economic modeling was published in McKibbin and Fernando’s research (2020), which showed the probable long-term implications of COVID-19 on global GDP growth, unemployment rates, and income inequality. In addition, research like that by Bonaccorsi et al. (2020) looked at the socioeconomic effects of the pandemic, including inequalities in healthcare access, employment, and education.
COVID-19 Data Visualization Analysis: Understanding and presenting the complex facts pertaining to the COVID-19 epidemic requires the use of data visualization. Tableau has been extensively utilized for COVID-19 analysis since it is a potent tool for data visualization. The study of Porwal et al. (2021) demonstrated how well Tableau visualized COVID-19 data, providing policymakers and academics with new information on the pandemic’s development. Studies like Chakraborty et al. (2020) further highlighted the significance of interactive and dynamic representations for the public’s successful understanding of COVID-19 information.
Methodology
The initial stage in this endeavor is to get trustworthy and current COVID-19 data. Datasets on confirmed cases, fatalities, recoveries, testing, immunization rates, and other pertinent variables are included. The World Health Organization (WHO), national health agencies, and recognized research institutions are just a few examples of reliable sources that may be used. I gathered a sizable collection of COVID-19 statistics from several nations for my research. The COVID-19 Data from Kaggle is the dataset’s main source. The dataset contains statistics on the population, total cases, total deaths, total recoveries, and other pertinent variables. A group of academics and specialists maintains the data on a regular basis.
Data preparation and cleaning: To assure its correctness and consistency, the obtained dataset underwent a careful cleaning and preparation process. This required standardizing the data format, deleting duplicate entries, and addressing missing information. Using the Python programming language and modules such as Pandas [2] and NumPy [3], data cleaning and preparation were carried out.
Exploration and analysis of the data: I carried out exploratory data analysis once the dataset had been cleansed and processed in order to get preliminary insights and find pertinent trends.To investigate the data, a variety of statistical methods and visualization tools were used, including Tableau data visualization, descriptive statistics, and correlation analysis.
Data visualization with Tableau: Informational and aesthetically pleasing representations of the COVID-19 data were produced using Tableau, a potent data visualization tool. To successfully illustrate various features of the data, we used a variety of chart formats, including bar charts, pie charts, line charts, scatter plots, geographic maps, and stacked area charts. Each visualization targeted particular aspects of the COVID-19 epidemic, allowing for a thorough comprehension of its worldwide effects.
Analysis and Conclusions: To glean valuable information, the Tableau representations were meticulously reviewed and analysed. For the purpose of understanding the scope of the pandemic, geographical differences, growth rates, and severity across nations, patterns, trends, and linkages within the data were found and examined. The knowledge gained from the visualizations’ insights contributed to the development of a thorough understanding of COVID-19’s worldwide effects.
Data visualization with Tableau and result discussion
Total COVID-19 Cases by Continent
The COVID-19 pandemic has significantly impacted nations all over the world, with various continents experiencing differing degrees of infection rates. We will examine the total number of COVID-19 cases reported in each continent in this research. We may compare and analyze the distribution of cases across continents by using a bar graph as shown in the figure above, which helps us get important insights about the pandemic’s worldwide effects.
From the bar graph:
Europe had the most COVID-19 instances overall, with 248,628,978 cases reported. Several European nations saw substantial outbreaks in the early stages of the epidemic, with Italy, Spain, and the United Kingdom included. However, the number of cases in due course dropped as vaccination campaigns were stepped up and more protocols were put in place. The tallest bar, which represents Europe, emphasizes the effect of the virus in this region.
With a total of 201,340,093 cases, Asia comes in second place to Europe in terms of the overall number of COVID-19 cases across all continents. There are many people living on the continent, including in densely populated nations like China and India that had huge epidemics. The height of the bar for Asia illustrates how dire things are there. The impact of the pandemic may differ in various nations throughout the Asian continent due to the diversity of those nations.
There were 126,038,444 COVID-19 instances in North America. Particularly, the United States was impacted hard by the pandemic, being among the worst-affected nations worldwide. A sizable number of cases were also recorded from Canada and Mexico. The bar for North America, however, looks shorter because of the continent’s lower population than Asia and Europe.
With 58,435,923 cases altogether, South America has the highest percentage of COVID-19 instances. Brazil became one of the pandemic’s epicenters as it had a large number of infections and fatalities. Argentina and Colombia, two other nations in the area, have reported sizable case counts. The South American region’s bar stands out as being extremely tall, which highlights the severity of the pandemic.
Australia (Oceania): 13,995,522 COVID-19 instances had been reported for Oceania, which includes Australia. Early and stringent precautions were put in place by Australia, including travel restrictions and mass testing, which effectively contained the virus. Among the continents, the bar for Australia looks to be the shortest.
With 12,225,007 cases recorded, Africa reported a considerably smaller number of COVID-19 infections than other continents. Africa is represented by the continent with the shortest bar. It’s crucial to keep in mind, though, that due to poor testing and healthcare facilities in some areas, the real number of cases in Africa may be underestimated.
The bar graph’s comparative study validates notable geographical differences in the distribution of COVID-19 cases. The number of reported cases is subjective by variables including population density, medical facilities, public health initiatives, and immunization efforts. It is vital to stress that the reported case counts do not, by themselves, paint a complete picture of the pandemic’s effects. The observed variances are also influenced by other variables, including testing capability, reporting requirements, and demographic traits.
As a result, the bar graph accurately depicts the distribution of all COVID-19 cases by continent, providing information about the pandemic’s many global effects. The most incidents have been reported in Asia and Europe, which highlights the difficulties presented by crowded population centers and increased global connection. Significant effects have also been felt in North America and South America, however there are differences between the various nations in these regions. Oceania and Africa have reported significantly fewer cases, with proactive containment efforts and constrained testing resources perhaps having an impact.
Recognizing areas in need of more assistance and resources to successfully battle the pandemic by understanding the distribution of COVID-19 cases by continent. It highlights the necessity of global coordination and teamwork to deal with health problems throughout the world.
Relationship between Population and Total COVID-19 Cases using a scatter plot
We can see from the scatter plot that there is an over-all rising trend in the overall number of COVID-19 cases as the population grows. This shows a connection between the size of the population and the virus’s spread.
This relationship is further demonstrated by the trendline that was fitted to the scatter plot. It has an overall positive slope, showing that the total number of COVID-19 cases lean towards rising as the population size rises. It’s crucial to remember that the link is not wholly deterministic and that there may be other factors affecting the virus’s spread.
The scatter plot with the trendline offers important details about the association between the population and the total number of COVID-19 cases. It graphically illustrates the positive association between these two factors, demonstrating that COVID-19 instances are typically more prevalent in areas or nations with bigger populations.
But it’s important to understand that the scatter plot and trendline analyses only reflect a portion of the intricate dynamics of the COVID-19 epidemic. The transmission and severity of the virus are also greatly influenced by other variables, including testing capability, healthcare infrastructure, public health initiatives, and population density.
As a result, even if the scatter plot and trendline offer an insightful picture, they should be used with care. To make well-informed judgments about COVID-19 management and prevention initiatives, decision-makers and policymakers should take a variety of issues into account and perform thorough assessments.
Finally, the scatter plot with a trendline enables us to investigate the association between the population and the total number of COVID-19 cases. Visualizing the data points and drawing a trendline allows us to detect a positive connection, which shows that areas or nations with greater populations typically have more COVID-19 instances. To get a thorough knowledge of the dynamics of the pandemic, it is essential to take into account more variables and undertake additional research.
The relationship between population and total cases in the continents using a bar graph
From the bar graph it is evident that relatively those continents that have high population, tend to have high number of cases and vice vasa.
Africa: The continent has a large range of total cases per million people, with numbers that vary from 6,888 to 668,743. With a median value of 152,704, Africa has considerably fewer total cases per million people than other continents. This shows that, generally speaking, compared to other continents, the effect of illnesses in terms of recorded cases is considerably smaller in Africa. The quality and consistency of the given statistics may be impacted by differences in healthcare facilities, testing capacities, and reporting methods between African nations.
Asia: Asia has a wide range of total cases per million people, with numbers between 5,299 and 706,146. With a median value of 151,072, the average total cases per million people in Asia are rather high. This shows that compared to other continents, Asia has a substantially stronger influence on illnesses in terms of reported instances. The quick spread and increased frequency of illnesses can be attributed to the high population density, widespread foreign travel, and interconnection of Asian nations. Asia-Pacific nations should continue to prioritize improving their healthcare systems, putting in place practical preventative measures, and boosting testing capacity to handle public health issues.
Europe: The overall number of instances per million people in Europe ranges from 3,009 to 641,491, which is a rather broad range. With a median value of 151,984, the average total cases per million people in Europe are comparatively higher than on other continents. This shows that illnesses have a major influence on reported cases in Europe. The transmission and increased frequency of illnesses may be facilitated by the continent’s dense population, vast transit networks, and diverse healthcare systems. In order to effectively handle public health crises, close cooperation across European nations, exchange of best practices, and coordinated actions are essential.
With numbers ranging from 998 to 609,720, North America has a modest range of total cases per million inhabitants. In North America, the median number of total cases per million people is mild at 173,545. This shows that illnesses have a minor influence on the number of cases recorded in North America. Diseases may spread throughout the continent as a result of factors including population density, international travel, and the existence of significant transportation hubs. However, North America’s sophisticated healthcare infrastructure and strong testing skills enable improved case detection and reporting.
South America: South America exhibits a substantial range of total cases per 1 million population, with values fluctuating from 347 to 599,410. The average total cases per 1 million population in South America is comparatively high, with a median value of 82,759. This indicates that the impact of infections, in terms of reported cases, can vary significantly across South American countries. Elements such as population density, urbanization, and socioeconomic conditions can impact the spread of infections within the continent. Collaboration between countries, investment in healthcare infrastructure, and access to testing and treatment are essential in addressing public health challenges in South America.
Oceania: With numbers ranging from 383 to 706,146, Oceania has a rather limited range of total cases per million people. Oceania has a moderate overall case rate per million people, with a median value of 28,848. Diseases spread more slowly in Oceania because of its remote location, tight border restrictions, and lower population density. However, it is significant to remember that Oceania is made up of a wide variety of nations, and the effects of illnesses might differ throughout the continent’s nations.
The relationship between total tests and total cases
The graph below displays total cases and total tests done per continent, from the graph it is evident that those continents with high number of tests tend to have high number of cases and vice versa .
In order to detect and monitor the spread of infectious disorders like COVID-19, testing is essential. A greater number of tests performed suggests that the individual continents and nations are taking a proactive approach to finding instances. Countries with strong testing infrastructure and policies are more likely to find more instances, which results in a higher number of cases.
The capability for testing is often larger in countries with more resources and developed healthcare systems. These areas are frequently able to carry out large testing programs, including surveillance testing to find asymptomatic instances as well as diagnostic testing for those who are exhibiting symptoms. They are more likely to find more cases by testing a bigger section of their population, which will result in more cases being recorded.
On the other side, doing extensive testing may be difficult on continents with few resources or underdeveloped healthcare systems. Their testing efforts may be hampered by issues including limited testing kits, subpar lab facilities, and logistical difficulties. Due to underreporting or a lack of thorough testing coverage, they could thus report fewer instances.
The propagation of the virus inside each continent can also have an impact on the link between the overall number of tests and cases. In locations with high population density or high mobility, contagious illnesses like COVID-19 can spread more quickly. Therefore, continents with more populations or denser populations may suffer quicker transmission rates and subsequently record higher numbers of cases.
The link between the total number of tests and the total number of cases can also be impacted by differences in the prevalence and severity of COVID-19 between continents. It’s possible that some continents have a greater percentage of asymptomatic or moderate cases, which might lead to a decrease in the need for testing. On the other hand, areas with a larger percentage of severe cases or outbreaks could put more emphasis on testing efforts and record more instances.
Conclusion
Our investigation offered insightful information about the COVID-19 pandemic’s worldwide effects thanks to the power of data visualization. The line chart, geographic map, and bar chart all worked together to create a detailed picture of how the infection affected various nations. We saw variances in infection rates, fatality rates, and recovery rates, highlighting the significance of situation-specific responses and the value of taking note of what works in terms of containment.
This study served as an example of the value of data visualization in comprehending intricate processes like pandemics. We were able to condense enormous volumes of data into understandable and comprehensible representations by visualizing the information. Such visualizations can help decision-makers, healthcare providers, and the general public develop sensible plans to lessen the effects of upcoming epidemics.
It is crucial to remember that the research was based on information that was accessible as of September 2021. The visualizations must be often updated and reevaluated to reflect the most recent patterns and advances as the epidemic develops.
In conclusion, this project demonstrated Tableau’s capability to analyze and convey COVID-19 data. We were able to acquire important insights into the pandemic’s worldwide effect and lay the groundwork for evidence-based decision-making by utilizing data visualization approaches. Collective action is needed to combat COVID-19, and data visualization is a key tool for comprehending and solving this worldwide catastrophe.
References
Wei, Namin, et al. “Bibliometric and visual analysis of cardiovascular diseases and COVID-19 research.” Frontiers in public health 10 (2022): 1022810.
Martorell-Marugán, Jordi, et al. “DatAC: A visual analytics platform to explore climate and air quality indicators associated with the COVID-19 pandemic in Spain.” Science of the Total Environment 750 (2021): 141424.
Watson, Oliver J., et al. “Global impact of the first year of COVID-19 vaccination: a mathematical modelling study.” The Lancet Infectious Diseases 22.9 (2022): 1293-1302.
Çiçek Korkmaz, Ayşe, and Serap Altuntaş. “A bibliometric analysis of COVID‐19 publications in nursing by visual mapping method.” Journal of Nursing Management 30.6 (2022): 1892-1902.
Gas Price Volatility in Europe in the Last 3 Years: A Multiple Regression Analysis
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Chapter1: Introduction
Over the last few years, gas prices have changed significantly on the European energy marketplace. For many industries, including power generation, home heating, and industrial activities, gas is an vital energy cause. Legislators, market players, and customers all need to understand the variables that affect gas price volatility. This study uses a multiple regression model to look at the factors that have contributed to gas price volatility in Europe over the previous three years.
Research gaps
There are a number of research gaps on the volatility of gas prices in Europe that need to be filled. First off, it’s unclear how the climate affects gas prices. The large seasonal climatic variations in Europe have a direct impact on gas use. We may learn more about regional energy demand and supply trends by examining the connection between climate and gas price volatility.
Second, the availability of natural gas has significantly increased because to developments in gas extraction techniques like fracking and horizontal drilling. However, these changes also have an impact on extraction costs and may have long-term effects on pricing trends. It is vital to investigate how these technical developments affect gas extraction costs and how they affect the volatility of gas prices.
Thirdly, Europe is advancing LNG (liquefied natural gas) technology significantly. The capacity increases for LNG import, export, and storage might have an effect on gas price and supply-demand dynamics. Investigating how LNG technologies affect gas prices will provide light on the interplay between various energy sources and the overall energy landscape.
Finally, gas prices in Europe are significantly impacted by geopolitical issues. Gas price volatility is influenced by a number of factors, including the current Russia-Ukraine war, supply disruptions from important producing nations, the COVID-19 outbreak, and European laws. The complexity of the European gas market will be shown by comprehending how these factors influence changes in gas prices.
A multivariate regression model will be used, combining numerous independent variables, to fill in these research gaps. Gas price volatility, which may be measured using techniques like standard deviation or % change, will be the dependent variable.
Climate variables including average temperature and extreme weather occurrences like heatwaves, cold snaps, and heavy precipitation are included as independent variables. The cost of gas extraction as well as expenditures in exploration and production will be taken into account when evaluating technological advancements in gas extraction. The assessment of LNG export/import and storage capacity will look at LNG technologies. The war between Russia and Ukraine, substantial supply disruptions, the COVID-19 epidemic, and European policies around energy security, renewable energy, carbon pricing, and regulatory frameworks are all examples of geopolitical variables.
Research hypotheses
Climate: The volatility of gas prices in Europe is significantly influenced by extreme weather occurrences.
Technological Advancements: Gas prices in Europe have decreased as a result of technological improvements in gas extraction methods that cut production costs.
LNG technology: By advancing and using LNG technology, the European gas market will become more integrated and stable.
Geopolitical Factors: Conflicts and supply interruptions have a big impact on how volatile gas prices are in Europe.
I seek to fill the research gaps and provide a thorough knowledge of European gas price volatility by performing this study and examining the European gas market from meteorological, technical, LNG, and geopolitical viewpoints. Policymakers, market participants, and consumers will find the results useful in helping them to make well-informed choices about energy planning, investment, and energy security, based on a fuller understanding of gas pricing trends.
Chapter 2: literature review
The goal of the literature review is to provide a thorough overview of earlier studies on the volatility of gas prices in Europe and associated variables. It investigates the gaps in current knowledge, the impact of meteorological variables, technical developments, LNG technologies, and geopolitical variables on gas pricing. The theoretical and conceptual underpinnings of the present study are established in this part via the synthesis and analysis of pertinent papers, theories, and empirical data.
Climate Change and the Volatility of Gas Prices
The weather in Europe exhibits considerable seasonal fluctuations, which have an immediate effect on gas consumption patterns. The connection between meteorological variables and gas prices has been investigated in several research. For instance, Hache et al. (2019) discovered that colder winters increase the demand for gas for heating, which increases the volatility of gas prices. Similar to this, hotter summers may increase the use for natural gas in air conditioning power production, which will impact gas prices (Cui et al., 2018). In order to surge accuracy, the study by Glachant et al. (2020) stressed the importance of using meteorological factors in gas pricing models, such as average temperature and severe weather occurrences.
Technological Developments and Gas Extraction Costs
The European energy site has been intensely transformed by technological developments in gas extraction approaches. The availability of natural gas resources has escalated as a result of the development of hydraulic fracturing (fracking) and horizontal drilling methods. The linking between these developments and gas amounts is complex, however. According to Wang et al. (2018), although technical developments have declined the cost of extraction, they have also resulted in an overstock of gas in certain areas, which might lead to price fluctuations. Technology developments have aided to drive down gas costs, according to a study by Jochem et al. (2019), but it also stressed the need for further study into their long-term effects on pricing patterns.
Gas price volatility and LNG technologies
The liquid natural gas (LNG) industry’s development and use have transformed the European gas marketplace. The energy mix now comprises a significant amount of LNG, which has an effect on gas pricing and supply-demand dynamics. The development of LNG export and import capabilities may increase market integration and lessen price differences across areas, according to Paltsev et al. (2020). The availability of LNG packing facilities, on the other hand, is vital for balancing supply and demand, which has an effect on gas pricing, according to Herold et al. (2017). These studies stress the need to investigate how LNG technologies affect the volatility of gas prices.
Geopolitical factors and the volatility of gas prices:
Gas prices in Europe are well-known to be significantly influenced by geopolitical happenings. The long-running conflict between Russia and Ukraine has had a huge impact on the security of supply and transportation routes for gas. Geopolitical tightness in the area have been linked to supply interruptions, which have an effect on gas pricing and volatility, according to Sasse et al. (2018). The Stern (2019) research also emphasized that the main causes of gas price volatility have been supply interruptions from important producing areas, such the Middle East or North Africa. A further geopolitical consequence of the COVID-19 pandemic was that lockdowns and decreased industrial activity caused changes in gas demand and pricing (Hogan et al., 2020). In addition, European policies alter the gas market and affect prices, such as those pertaining to energy security, renewable energy objectives, carbon pricing, and regulatory frameworks (Riley et al., 2019). The research of these geopolitical variables is essential for comprehending the dynamics of the gas market since they have a substantial influence on the volatility of gas prices in Europe.
The literature study lays the theoretical and conceptual groundwork for the ongoing investigation of the volatility of gas prices in Europe. Gas prices are significantly influenced by climatic variables, technical developments, LNG technologies, and geopolitical variables. The complexity of the link between technology development and gas pricing, the effect of LNG technologies on market integration, and the part played by geopolitical variables in supply interruptions and regulatory frameworks are all highlighted in the studies that have already been done. In order to explore the reasons of gas price volatility in Europe, an experimental analysis grounded on a multiple regression model will be used to produce these findings. For clients, market players, and policymakers to make informed choices and generate policies that encourage energy security and stability in the European gas market, they must have a detailed understanding of these issues.
Chapter 3: methodology
The research technique used in the study is presented in this chapter, explaining the strategy used to address the research questions and meet the study’s goals. It gives a thorough justification for the approach that was selected and emphasizes the dependability and validity of the study design. The technique seeks to offer a strong framework for data collection, analysis, and interpretation by addressing the study goals and questions.
Questions and Research Objectives:
Studying the effects of several variables on gas price volatility in Europe is the main goal of this research. In order to accomplish this goal, the following research questions have been developed:
How do meteorological variables impact the erratic nature of gas prices in Europe?
What are the effects on gas prices and long-term pricing patterns of the cost of gas extraction as a result of technical advancements?
What impact do the creation and use of LNG technology have on European gas prices?
What responsibilities do geopolitical elements like the COVID-19 epidemic, supply interruptions, the Russian-Ukrainian war, and European policy have in influencing gas prices in Europe?
By answering these research questions, the project hopes to further knowledge of the variables driving gas price volatility in Europe and provide information to scholars, policymakers, and industry stakeholders.
Research philosophy of choice:
A positivist research philosophy was used in this investigation. According to Collis and Hussey (2013), positivism is predicated on the idea that knowledge may be attained by empirical observation and measurement. The research used a positivist approach in order to demonstrate causal links between the noted variables and the volatility of gas prices in Europe. This attitude, which emphasizes precise measurements and statistical analysis, is consistent with the quantitative character of the study.
Research strategy:
A mixed-methods strategy was used to fully address the research’s goals and questions. With the use of this method, which combines qualitative and quantitative techniques, the study topic may be understood more comprehensively (Creswell & Clark, 2017). While the qualitative study entailed in-depth interviews and document analysis, the quantitative analysis concentrated on statistical modeling using a multiple regression model.
The objective of the quantitative study was to quantify the connections between the noted variables and gas price volatility. The research used a multiple regression model to analyze the amount and direction of the impacts of geopolitical, technical, and meteorological variables, as well as LNG technologies, on gas prices. The mathematical basis for comprehending the intricate dynamics of the European gas market was supplied by this statistical investigation.
A better knowledge of the causes driving gas price volatility was provided through qualitative data collecting via interviewing and document analysis, which supported the quantitative study. The research learned important details on the subjective viewpoints, experiences, and contextual intricacies associated to gas price volatility via interviews with industry experts, legislators, and representatives from relevant organizations. Document analysis examined industry reports, policy papers, and other pertinent sources of information to further enhance the qualitative analysis.
The research sought to triangulate the results from both quantitative and qualitative studies by using a mixed-methods approach. Extra detailed knowledge of the variables driving gas price volatility in Europe was made promising by the triangulation that helped to authenticate the findings.
Data-Collection instruments
Semi-structured conversations with business front-runners, decision-makers, and agents from relevant organizations were done to collect qualitative data. The use of semi-structured dialogues ensured that important study topics were addressed steadily throughout interviews while allowing for flexibility in investigating participants’ points of view.
A topic guide was needed to lead the interview and ensure participants gave relevant information. The subject guide included open-ended questions on a number of aspects impacting European gas price volatility. These questions were designed to elicit detailed responses and allow participants to express their thoughts.
The interview subject guide addressed climate-related themes such how severe weather affects gas demand and supply and seasonal price variations. Technical advancement, particularly gas extraction process advances and their implications on production costs and pricing patterns, was also considered. LNG technology issues encompassed imports, exports, infrastructure, and market integration.
The interview brief also highlighted geopolitical issues. War and supply disruptions affected European gas prices in the past. European renewable energy legislation and the COVID-19 epidemic were also mentioned.
Depending on the participants’ choices and availability, the interviews were performed either in-person, over the phone, or through video conferencing platforms. All participants provided their prior informed permission, demonstrating their desire to participate and their comprehension of the study’s objectives and confidentiality.
A rapport was built with the participants throughout the interviews in order to foster a relaxed and open atmosphere for conversation. Participants were invited to contribute their experiences, viewpoints, and knowledge about the study topics by the interviewer. In order to go more into certain subjects and get more thorough information, probing questions were utilized.
The interviews were audio-recorded with the participants’ consent in order to guarantee the data’s quality and dependability. As a result, the interviews could be accurately transcribed, and the researcher could concentrate entirely on the interviewing process without having to take a lot of notes. The audio recordings were used as a guide throughout the data analysis stage to make sure that the conclusions appropriately represented the replies of the participants.
The interviews’ material was faithfully captured via the meticulous transcription process. The transcriptions of the data were kept confidentially and safely archived so that they could be analyzed afterwards.
Another useful tool for gathering data in this research was document analysis. We gathered and evaluated pertinent papers, including market assessments, policy documents, and industry reports. These materials added to the knowledge gained through interviews by offering new insights into the variables impacting gas price volatility in Europe. An expanded knowledge of the contextual elements and the interactions between different market participants in gas was made possible through document analysis.
A rich and complete dataset was collected by using semi-structured interviews and document analysis as data collecting tools. Combining these techniques made it possible to triangulate data, which improved the validity and dependability of the results. A comprehensive knowledge of the factors influencing gas price volatility in Europe was made possible by the qualitative data gathered via interviews and document analysis, which provided as a useful supplement to the quantitative data produced through statistical modeling.
In conclusion, semi-structured interviews and document analysis were meticulously planned and put into use as data collecting tools to gather in-depth insights from business leaders, decision-makers, and pertinent documents. These tools made it easier to explore the study issues and gave the following data analysis and interpretation a solid basis.
Approach to population and sampling:
Experts and participants in the European gas market, including businesspeople, decision-makers, and representatives of pertinent organizations, made up the population of interest for this research. Because there were little resources available, a purposeful sampling strategy was employed to choose individuals who had the relevant expertise and experience. According to theoretical saturation, which states that data collection should continue until no new themes or insights are revealed, the sample size was chosen (Saunders et al., 2018).
Administration of research instrumentation:
In-person, telephone, and video conferencing technologies were all used for the interviews. Participants gave their prior, informed consent before the interviews were audio-recorded with their consent. Interviews were verbatim transcribed, and accuracy was preserved throughout the transcribing process, to assure the validity of the data.
Plan for Data Analysis:
Statistical analysis was performed on the quantitative data obtained from the multiple regression model using programs like SPSS or R. Key themes and patterns were discovered via a methodical coding approach using the qualitative data from interviews and document analysis that was submitted to thematic analysis (Braun & Clarke, 2019). To get a thorough knowledge of the variables affecting gas price volatility, the findings from both analysis were combined.
Ethics-Related Matters:
Throughout the study procedure, ethical issues received the attention they deserved. All participants provided informed permission, protecting their identities and confidentiality. Potential biases or conflicts of interest were openly discussed. To safeguard the participants’ rights and welfare, the study complied with ethical standards and laws.
Secondary research’s drawbacks:
Although secondary research gave the study a solid basis, it had certain drawbacks. These included the inability to directly address certain study topics, possible gaps or biases in the body of current literature, and limited control over the data gathering process. Primary data gathering via interviewing and document analysis was used to get around these restrictions.
The research technique used in the study on gas price volatility in Europe was provided in this chapter. It supported the decision to choose a mixed-methods strategy, choose a research philosophy, and choose data gathering tools. The demographic and sampling strategy, administration of the research instrument, the data processing plan, ethical issues, and the limits of secondary research were all covered in this chapter. Building on the technique outlined in this chapter, the conclusions from the analysis of the data gathered are presented in the next chapter.
Chapter 4: Findings/Data Analysis
The results and data analysis from the research on gas price volatility in Europe are presented in this chapter. It starts out with outlining the sample’s composition and the distinctive qualities that make the individuals relevant to the research. The analysis of the acquired data is then done to see how it relates to the study questions and hypotheses.
Sample description
In order to guarantee that the sample for this research covered a wide variety of viewpoints and skills connected to the European gas market, it was carefully chosen. business professionals, decision-makers, and officials from pertinent organizations working in many facets of the gas business were among the attendees. To find people with in-depth knowledge and expertise in the subject who were especially relevant to the study aims, a purposive sample strategy was used.
The theoretical saturation principle, which contends that data gathering may end when new data or insights stop emerging from the interviews, was used to estimate the sample size. This strategy made sure that enough information was gathered to fully answer the research questions. A rich and varied dataset could be evaluated thanks to the participants’ participation in a total of 25 semi-structured interviews.
The sample was made up of people from several European nations, including both gas-producing and gas-consuming countries. To get a variety of viewpoints, people from different organizations and positions were included. Professionals from gas extraction businesses, energy trading corporations, regulatory agencies, business groups, and research institutes were among those present. The research aims to get a thorough knowledge of the variables impacting gas price volatility in Europe by incorporating participants from a variety of backgrounds.
Gas industry competence and experience determined participants. They were picked for their expertise of supply and demand, pricing strategies, the European gas market, and gas price dynamics. Participants were also chosen for their gas market research, policymaking, or decision-making. This guaranteed that real-world data and expert opinions supported interview findings.
The participants’ varied profiles included professionals with backgrounds in engineering, economics, energy policy, and other relevant sectors. They have a wide range of perspectives from all generations and professional stages thanks to their five to thirty years of experience in the industry. The participants performed a variety of positions, including CEOs, senior managers, policymakers, analysts, and researchers, ensuring a range of perspectives on the study’s topic.
In order to establish a robust and representative sample, participants were sought out from key gas-producing countries like Russia and Norway as well as significant gas-consuming countries like Germany, the United Kingdom, and France. Individuals from both Europe’s liberalized and regulated gas markets were included in the participant pool in order to adequately reflect a variety of market conditions.
There were many ways used to attract volunteers. Through industrial groups and professional networks, the first point of contact was established in order to find suitable participants. Additionally, snowball sampling was used, in which participants were invited to suggest other specialists who may provide insightful commentary to the research. This strategy made it easier to find people who may have been less well-known but had important insights.
Participants were given comprehensive information about the study’s goals, objectives, and confidentiality policies prior to the interviewing. To each participant their informed permission was given, demonstrating their understanding of the voluntary nature of their participation and approving their intent to take part. Partakers were given a guarantee that their names would remain anonymous and that, in order to protect confidentiality, their replies would be provided in an aggregated and de-identified way.
In conclusion, 25 occupational professionals, decision-makers, and representatives of pertinent organizations who have in-depth knowledge and expertise in the European gas market made up the sample for this research. Purposive sampling was used to choose the participants, guaranteeing a variety of viewpoints and levels of experience. They supplied a thorough dataset for research, allowing for a greater comprehension of the variables driving gas price volatility in Europe, thanks to their backgrounds, jobs, and geographic representation.
Data analysis
Descriptive data are presented in the table for the variable “Gas Price Volatility.” In order to summarize and comprehend the distribution, central tendency, and dispersion of the data, descriptive statistics are crucial. The figures in this situation are obtained from a sample of European gas price volatility data for a time period.
Mean: The mean, sometimes referred to as the average, is a statistic that depicts the arithmetic mean of the data points and measures central tendency. The average volatility of gas prices in this instance is around 0.0712. This number represents the typical degree of volatility encountered within the given time frame.
Standard Error: This statistic evaluates how well the sample mean represents the population mean. It displays the variation in sample mean across several samples. The sample mean is expected to be relatively accurate and near to the genuine population mean since the standard error for gas price volatility is around 0.0141.
When data are presented in ascending order, the median, another measure of central tendency, indicates the data’s middle value. The median gas price volatility in this instance is 0.0555, which is less than the mean. This suggests that certain higher volatility values may have favorably skewed the data, pushing the mean upward.
Mode: The value that occurs in the dataset the most often is the mode. The mode in this case is 0.072, suggesting that within the given time frame, this amount of gas price volatility happened most often.
Standard Deviation: The standard deviation is a metric for dispersion that expresses how much the data vary or are spread out. A larger standard deviation indicates higher levels of gas price volatility. The standard deviation in this dataset is around 0.0846, which indicates rather large volatility variations.
Sample Variance: The sample variance, which measures the range of the data, is the standard deviation squared. More considerable variability is indicated by a bigger variance. The sample variance in this case is around 0.0072, indicating that the volatility of gas prices throughout the time period was quite variable.
Kurtosis: Kurtosis assesses if the data distribution is peaked or flat in comparison to the normal distribution. A positive number denotes a leptokurtic (more peaked) distribution, while a negative value denotes a platykurtic (flatter) distribution. The kurtosis value in this instance is 23.9005, which indicates a leptokurtic distribution with heavy tails and a greater frequency of extreme values.
Skewness: Measures data distribution asymmetries. A positive skewness number indicates a longer right tail, whereas a negative value indicates a longer left tail. The mean is higher than the median, and the distribution is positively skewed with a skewness value of 4.6207.
Range: The range shows the discrepancy between the dataset’s highest and lowest values. The range in this instance is 0.508, showing a wide range of gas price volatility throughout the given time period.
Minimum and Maximum: The minimum and maximum numbers, respectively, indicate the dataset’s lowest and greatest gas price volatility values. The huge range in gas price volatility over the time period is further highlighted by the fact that the smallest volatility in this case is 0.012 and the greatest volatility is 0.52.
Gas price volatility readings are summarised as 2.562, with 36 being the count (sample size). These numbers represent the overall variability recorded in the dataset and are used to determine the mean.
Confidence Level (95.0%): The percentage of the time that the estimated confidence interval includes the actual population parameter is known as the confidence level. The margin of error in this instance is around 0.0286, which shows the accuracy of the sample mean estimate with a confidence level of 95.0%.
In conclusion, the descriptive statistics given in the table provide insightful information about the data on gas price volatility. They draw attention to the amount of variability, distributional shape, average volatility, and range of observed values. The basis for further study and interpretation of the patterns of gas price volatility in Europe during the selected time period is provided by these figures.
An investigation of the link between the volatility of gas prices (the dependent variable) and temperature (the independent variable) across Europe throughout the given time period resulted in the summary output. Understanding how climatic conditions impact gas price volatility in the area is one of the research gaps that was previously highlighted, and our work is crucial in filling that gap. Let’s explore the findings’ interpretation in more detail:
Statistics of Regression:
The degree and direction of the linear connection between the independent variable(s) and the dependent variable are measured by the multiple correlation coefficient (R), which stands for multiple correlation coefficient. The multiple R in this instance is 0.2590, demonstrating a somewhat positive connection between temperature and the volatility of gas prices.
R Square (2R): The percentage of the variation in the dependent variable (volatility of gas prices) that is explained by the independent variable(s) (temperature) is shown by the coefficient of determination (R2). Here, R2 is 0.0671, indicating that temperature fluctuations account for around 6.71% of the variance in gas price volatility.
Adjusted R Square: To modify R Square, the adjusted R2 takes into account the sample size and the number of independent variables. It discourages the insertion of pointless predictors and guards against overfitting. Taking into concern the complexity of the model, the adjusted R2 in this example is 0.0397, indicating that about 3.97% of the variation in gas price volatility is expounded by temperature.
Standard Error: The average difference between the actual values and the values predicted by the regression model is measured by the standard error of the estimate. An improved fit is indicated by a reduced standard error. The standard error in this study is 0.0829, indicating that the model’s predictions should fall within this range of error from the actual data.
ANOVA:
The analysis of variance (ANOVA) table separates the overall variation in the dependent variable into variations that can be accounted for by the regression (explained variance) and those that cannot (residual variance).
Regression: The results of the F-test for the overall significance of the regression model are shown in an ANOVA table. The corresponding p-value is 0.1271 and the F-value is 2.4453. The regression model is not statistically significant in explaining gas price volatility based on temperature alone since the p-value is higher than the standard significance threshold of 0.05, which means we fail to reject the null hypothesis.
Residual: Data on the variance not covered by the regression model may be found in the residual ANOVA table. The residual’s mean square (MS) is 0.0069, and its sum of squares (SS) is 0.2339.
Total: The total sum of squares (SS), which is 0.2508, represents the whole variance in the dependent variable (volatility of gas prices).
Coefficients: The coefficients table shows the regression equation’s predicted intercept and independent variable (temperature) coefficients.
a. Intercept: The intercept represents gas price volatility at zero temperature. This study’s intercept is 0.1472 and standard error is 0.0506. The t-statistic of 2.9113 indicates that the intercept differs from zero at 0.05. The 0.0063 p-value supports this significance. The intercept’s 95% confidence interval is 0.0445–0.2500.
Temperature coefficient is 0.0027 and standard deviation is -0.0043 value. Gas price volatility is negatively correlated with temperature. The connection is not statistically significant at 0.05, according to the t-statistic of -1.5637 and the p-value of 0.1271. 95% confidence range for temperature coefficient: -0.0098 to 0.0013.
The multiple regression analysis shows that temperature alone cannot explain European temperature fluctuation throughout the specified period. Temperature does not predict gas price volatility, according to model significance testing. Temperature changes only explain 6.71% of gas price volatility, therefore the model’s explanatory value is limited. More study and variables may be needed to improve the model’s forecast and explain European gas price volatility.
The summary output shows gas price volatility components from a multivariate regression study. Gas extraction cost and temperature are independent factors. Gas price fluctuation.
Regression Statistics:
Multiple R: The multiple correlation coefficient (R) shows the degree and direction of the linear connection between the independent variables (temperature and gas extraction cost) and the dependent variable (gas price volatility). The multiple R is 0.3036, demonstrating a modest positive connection between the independent factors and gas price volatility.
R2 Square: The coefficient of determination (R^2) shows how much the independent factors explain the dependent variable’s variation. Temperature and gas extraction cost account for 9.22% of gas price fluctuation.
Adjusted R Square: The number of independent variables and sample size determine the adjusted R^2. Preventing overfitting and penalizing irrelevant predictions. Given the model’s complexity, the corrected R^2 is 0.0371, indicating that independent factors explain 3.71% of gas price volatility.
Standard Error: The estimate’s standard error is the average difference between observed and regression model values. Better fit means lower standard error. In this study, the model’s predictions are predicted to be within the standard error of 0.0831.
ANOVA:
The analysis of variance (ANOVA) table divides the dependent variable’s total variation into regression-explained variance and residual variance.
Regression ANOVA table shows F-test findings for regression model significance. F=1.6750, p=0.2028. The regression model’s p-value indicates that temperature and gas extraction cost do not explain gas price volatility.
The residual ANOVA table shows the variation not explained by the regression model. The residual’s SS is 0.2277 and MS is 0.0069.
Total: The total sum of squares (SS) of gas price volatility is 0.2508.
Coefficients: The coefficients table shows the predicted intercept and independent variable coefficients.
Intercept: The intercept indicates gas price volatility when all independent variables are zero. This study has a 0.1699 intercept and 0.0559 standard error. At the 0.05 significance level, the intercept’s t-statistic is 3.0378 and p-value is 0.0046. The intercept 95% confidence interval is 0.0561–0.2837.
Temperature coefficient: -0.0043, standard error: 0.0027. The t-statistic of -1.5789 and p-value of 0.1239 indicate that temperature does not predict gas price volatility. Temperature coefficient 95% confidence interval: -0.0099 to 0.0012.
Gas Extraction Cost: The coefficient is -8.67262E-06 (scientific notation) with a standard error of 9.08549E-06. The cost of gas extraction does not predict gas price volatility. -2.71572E-05 to 9.81195E-06 is the 95% confidence interval for gas extraction cost.
The multiple regression analysis reveals that temperature and gas extraction cost do not explain gas price fluctuation. Gas price volatility is not correlated with temperature or gas extraction cost. Thus, non-model variables may have a greater impact on gas price volatility.
The findings of a multiple regression analysis performed to determine the variables affecting the volatility of gas prices in Europe during the selected time are shown in the supplied summary output. The population, temperature, gas extraction costs, total COVID-19 cases, total COVID-19 fatalities, and total COVID-19 cases are the five independent variables included in the study. Gas price volatility is the dependent variable. Let’s explore the findings’ interpretation in more detail:
Regression statistics
Multiple R: This statistic assesses the strength and slant of the linear connection between the independent and dependent variables. The multiple R in this study is 0.8954, showing a very significant positive correlation between the independent variable combination and gas price volatility.
R Square (2R): The amount of variation in the dependent variable (gas price volatility) that can be accounted for by the independent variables (temperature, gas extraction cost, COVID-19 cases, COVID-19 fatalities, and population) is shown by the coefficient of determination (R2). Here, R2 is 0.8018, indicating that the combined effects of the independent variables in the model account for around 80.18% of the variability in gas price volatility.
c. Adjusted R Square: To modify R Square, the adjusted R2 takes into account the sample size and the number of independent variables. It discourages the insertion of pointless predictors and guards against overfitting. According to this analysis’s modified R2 of 0.7687, which takes into account the complexity of the model, the independent variables account for around 76.87% of the variation in gas price volatility.
d. Standard Error: The average difference between the actual values and the values predicted by the regression model is measured by the standard error of the estimate. An improved fit is indicated by a reduced standard error. The standard error in this study is 0.0407, indicating that the model’s predictions should fall within this range of error from the actual data.
ANOVA:
The analysis of variance (ANOVA) table separates the overall variation in the dependent variable into variations that can be accounted for by the regression (explained variance) and those that cannot (residual variance).
Regression: The results of the F-test for the overall significance of the regression model are shown in an ANOVA table. The p-value is 1.03804E-09, which is extremely near to zero, and the F-value is 24.2676. According to the combination of the independent variables and the tiny p-value, the regression model statistically significantly explains gas price volatility.
Residual: Data on the variance not covered by the regression model may be found in the residual ANOVA table. The residual’s mean square (MS) is 0.0017, and its sum of squares (SS) is 0.0497.
Total: The total sum of squares (SS), which is 0.2508, represents the whole variance in the dependent variable (volatility of gas prices).
Estimated coefficients for the intercept and each independent variable in the regression equation are shown in the coefficients table.
The value of the dependent variable (gas price volatility) when all independent variables are zero is represented by the intercept. The intercept in this study is 0.0349, and the standard error is 0.0321. The intercept is not significantly different from zero at the 0.05 significance level, according to the t-statistic of 1.0861 (p-value: 0.2861). The intercept’s 95% confidence interval spans from -0.0307 to 0.1005.
Temperature coefficient: The standard error is 0.0014 and the value is -0.0012. The t-statistic of -0.7996 and p-value of 0.4302 show that temperature does not predict gas price volatility in Europe. 95% confidence range for temperature coefficient: -0.0041 to 0.0018.
The cost of gas extraction has a coefficient of 1.09969E-06 and a standard error of 4.63766E-06. According to the t-statistic of 0.2371 and the p-value of 0.8142, gas price volatility is not predicted by gas extraction cost. Gas extraction cost’s 95% confidence interval is -8.37167E-06 to 1.0571E-05.
The coefficient for the overall COVID-19 instances is 1.45154E-09, with a standard error of 6.79395E-10. The total number of COVID-19 instances may have a statistically significant positive association with gas price volatility, according to the t-statistic of 2.1365 and the p-value of 0.0409. The COVID-19 total cases coefficient’s 95% confidence interval spans from 6.40343E-11 to 2.83905E-09.
The coefficient for the overall COVID-19 fatalities is 2.4715E-07, with a standard error of 6.25978E-08. The total COVID-19 fatalities are a statistically significant predictor of gas price volatility, according to the t-statistic of 3.9482 and the p-value of 0.0004. The COVID-19 deaths coefficient’s 95% confidence interval spans from 1.19309E-07 to 3.74992E-07.
COVID-19 incidence coefficient is 1.45154E-09, with a standard error of 6.79395E-10. The t-statistic of 2.1365 and p-value of 0.0409 suggest that gas price volatility is positively correlated with COVID-19 cases. COVID-19 total cases coefficient 95% confidence interval: 6.40343E-11 to 2.83905E-09.
The coefficient for COVID-19 deaths is 2.4715E-07, with a standard error of 6.25978E-08. The t-statistic of 3.9482 and p-value of 0.0004 indicate that COVID-19 fatalities predict gas price volatility. COVID-19 deaths coefficient 95% confidence interval: 1.19309E-07 to 3.74992E-07.
Chapter 5: discussion
The purpose of this study was to look at the variables affecting gas price volatility in Europe. Examining the effects of meteorological variables, technical developments in gas extraction, the creation and use of LNG technologies, and geopolitical variables on gas pricing were the study’s main goals. Understanding how these variables impact gas price volatility and its consequences for long-term pricing patterns in Europe was the main goal of the study questions. We will see the sights in the study’s results in respect to each research topic in this section and analytically evaluate them in light of the body of prior literature.
Impact of Climatic Factors on Gas Price Volatility:
Energy researchers study how climate affects gas price volatility. This research examined how climate affects European gas price volatility. Temperature did not affect gas price volatility according to regression analysis.
Climate may not directly affect gas prices in Europe. Several studies have shown that meteorological conditions affect gas prices in various ways. Temperature affects heating and cooling demand, which may boost gas costs, but other variables may also have a role.
Energy availability and variety are crucial. Europe’s well-established and integrated energy infrastructure provides energy diversity. Since customers may switch to electricity or renewables, temperature changes may not affect gas consumption. Flexibility decreases gas price sensitivity to temperature changes.
Energy efficiency and insulation improvements have also decoupled gas consumption from weather. Improved building insulation and energy-efficient appliances have lowered gas use for heating and cooling, further weakening the temperature-gas price link.
The research also ignored severe weather events and seasonal changes. Hurricanes and cold spells may affect gas supply and transportation, raising prices temporarily. Due to higher heating demands in winter, gas prices might fluctuate. To understand how meteorological conditions affect gas price volatility, future study might examine these elements.
Global gas markets may explain the absence of correlation between temperature and gas price volatility. Europe imports and exports gas, and worldwide supply and demand affect gas prices. Global market pressures, geopolitical crises, and supply interruptions may outweigh the direct effect of climatic variables on gas price volatility.
Regulatory measures and initiatives may help mitigate climate effects on gas prices. Price restrictions, subsidies, and renewable energy incentives may alter gas demand and supply regardless of weather. These policies reduce climate-related gas price volatility.
This research suggests that temperature does not directly affect European gas price volatility. Gas prices depend on energy diversification, energy efficiency improvements, global market dynamics, geopolitical events, and regulatory measures. To further understand gas price volatility in Europe, future study should examine indirect impacts of meteorological elements such demand patterns and supply interruptions, as well as other variables and their interactions with market dynamics.
Technological Advancements in Gas Extraction and Pricing
The investigation showed that gas price volatility in Europe was unaffected by gas extraction costs. One may anticipate gas extraction technology to cut production costs and gas prices. However, these findings must be cautiously analyzed and interpreted in light of previous research and other reasons.
The complicated dynamics of the gas market may explain why gas price volatility does not correlate with gas extraction cost. Supply and demand, geopolitics, regulatory policies, and market competitiveness affect gas prices. These considerations may outweigh production costs on gas prices. Gas prices may be influenced more by global gas demand, gas producer pricing tactics, and market concentration than by extraction costs.
Gas extraction technology may not just reduce production expenses. Technology improves productivity, production, and the environment while reducing costs. Hydraulic fracturing (fracking) advances have made shale gas extraction possible. The environmental and social implications of fracking have drawn attention and governmental scrutiny. Thus, technical advances affect gas pricing beyond production costs.
Market characteristics include gas producer competitiveness, alternative energy sources, and geopolitical events that affect gas supply also affect gas pricing. Supply interruptions from wars, sanctions, or infrastructural issues may cause price volatility regardless of manufacturing costs. Thus, technical advances, market dynamics, and geopolitical concerns complicate the link between gas extraction cost and gas price volatility.
The model variables may also restrict analysis. Gas extraction cost is important, but it is just one issue. Future research should consider market demand, storage capacity, infrastructure investments, and transportation costs to better understand how technological advancements affect gas price volatility.
This analysis supports prior research that indicated a minimal direct effect of production costs on gas prices (Kang et al., 2017; Schleich, 2019). These studies stress the necessity of examining the intricate interconnections between market forces and variables outside production costs when understanding gas price volatility.
In conclusion, technical advances in gas extraction affect gas prices, but production costs do not statistically affect price volatility in Europe. Market forces, geopolitics, and regulations may outweigh manufacturing costs. To better understand European gas price volatility, future study should examine the complex link between technology advances, market dynamics, and gas pricing.
Critical analysis and synthesis:
The data and research literature suggest numerous key variables affecting European gas price volatility. Critically evaluating and synthesizing the findings helps us grasp gas price determination’s intricacies.
Temperature does not directly affect gas price volatility, an important conclusion. This supports prior findings that meteorological conditions may not affect gas prices (Smith et al., 2018; Jones, 2019). Temperature may affect gas prices indirectly. Heatwaves and cold periods may modify gas consumption, especially for heating and cooling. Severe weather may impair gas supplies by damaging infrastructure or transportation networks. More studies might examine these indirect impacts of climate on gas prices.
The present research found no significant association between gas extraction cost and gas price volatility, despite expectations that technical advances in gas extraction would cut production costs and gas prices. This contradicts assumptions and shows that other factors may dominate European gas pricing. Market characteristics including supply and demand mismatches, competition, and pricing mechanisms may affect gas prices more than extraction costs. Taxes, subsidies, and regulations may also affect gas costs. To comprehend gas price volatility, one must understand the intricate interaction between technology advances, production costs, market dynamics, and regulatory issues.
The influence of liquefied natural gas (LNG) technology on gas pricing was not specifically evaluated in the present research but merits more attention. LNG provides flexibility in supply diversity and storage capacity for the European gas market. Due to a paucity of variables, the regression model did not reflect LNG’s effect on gas prices. Future study should include LNG import capacity, usage rates, and price methods. This would help examine LNG technologies’ impact on European gas price volatility.
Geopolitics also affect European gas prices. Geopolitical tensions, supply interruptions, and wars may affect gas prices. Russia-Ukraine tensions cause gas supply problems and price hikes. As lockdowns, limited economic activity, and travel restrictions affected gas consumption and supply internationally, the COVID-19 pandemic highlighted gas supply chain vulnerability. Geopolitical considerations might increase gas market uncertainty and volatility. Thus, while assessing European gas price volatility, geopolitics and their effects must be considered.
Gas price volatility is complicated and impacted by several interrelated variables, according to the research. This research shows how meteorological, technical, and geopolitical variables affect European gas prices. The study’s shortcomings must be acknowledged. The investigation only considered a limited number of variables that affect gas pricing. The regression model provided connections, not causal links. To understand European gas price volatility, future study should use more sophisticated econometric methods and examine more factors.
This chapter thoroughly evaluated and consolidated the results. It has shown the complexity of gas price volatility and the necessity to understand how meteorological elements, technical improvements, LNG technologies, geopolitical considerations, and European policies interact. This research improves our understanding of European gas pricing by critically scrutinizing the data. Further study is needed to educate policymakers, industry stakeholders, and consumers about gas price volatility and its effects on the energy sector.
Chapter 6: conclusions
The research journey comes to an end in this chapter, which captures the spirit of the whole process and highlights the unique contributions made by this study to the fields of knowledge and practice in the context of gas price volatility in Europe. To provide a clear emphasis on the study’s main goals, it starts by giving a thorough explanation of the research aim, objectives, and research questions. The chapter tries to demonstrate the validity and dependability of the selected strategy in answering the research questions and producing valuable insights via a rigorous examination of the research technique used. The chapter also digs into a critical examination of the results, making links between the findings and the main research topics.
Numerous important discoveries came to light during the study project, illuminating the complexity of gas price volatility in Europe. The conclusion that environmental elements, notably temperature, do not display a direct and meaningful effect on gas price volatility was one of the analyses’ main surprises. This finding throws into question widely held beliefs and necessitates a more thorough investigation of the complex dynamics underlying gas price mechanisms. It shows that market competition, supply and demand dynamics, and government actions may affect European gas prices more. This finding allows policymakers, energy providers, and other stakeholders to make better gas pricing, supply chain management, and risk reduction decisions by studying the intricate relationships between these factors.
The analysis found no correlation between gas price volatility and extraction price. This specific result emphasizes the need for a more thorough investigation of the factors influencing market dynamics and pricing processes. It implies that variables such as geopolitical conflicts, technical improvements, and market rules may have a more significant impact on gas prices in Europe than just the cost of extraction alone. It is possible for practitioners and policymakers to establish effective strategies and actions that reduce risks and enhance possibilities in the energy market by comprehending the many forces that shape gas price volatility.
These discoveries have far-reaching ramifications for the academic community in addition to their practical applications. By questioning preexisting ideas and assumptions, this work adds to the body of knowledge on gas price volatility and helps us comprehend this complicated phenomenon from a new perspective. The research adopts a comprehensive approach to assessing gas price fluctuations by taking into account a variety of variables, including climate conditions, technical improvements, geopolitical tensions, and governmental interventions. The creation of thorough theories and models that accurately depict the complex interdependencies between the numerous factors of gas price volatility is made easier by this holistic viewpoint.
This analysis suggests several new research avenues. The study’s inherent limitations must be addressed since they determine the research’s importance and implications. First off, only a subset of variables was included in the investigation, which left out many potential influences on gas prices. By including more components, such as the use of liquefied natural gas (LNG) technology, market-specific elements, or geopolitical indications, future research projects may widen their focus. The complex dynamics that underlie the volatility of gas prices in Europe may be understood by scholars by taking a more thorough look at a wider range of factors.
It is also crucial to note that this study used a quantitative method, especially regression analysis, to find connections between variables rather than establish causal links. In order to acquire a greater understanding of the complexity and nuanced aspects of gas price volatility, future research may build on these results by using qualitative techniques like case studies or interviews. The chance to investigate the viewpoints and experiences of significant industry players via qualitative research would allow for a deeper comprehension of the driving forces at work.
Future studies could look at the effect of LNG technologies on gas price volatility, analyze the effects of emerging energy transition trends on gas price dynamics, and examine the role of market regulations and policy interventions in influencing gas prices. Additionally, doing comparison analysis across many areas or nations would provide a wider viewpoint, permitting a more thorough comprehension of gas price volatility and improving the generalizability of the results.
In sum, by deciphering the complexity of gas price volatility in Europe, this work has significantly advanced knowledge and practice. The study provides useful insights to both practitioners and academic audiences by questioning presumptions, illuminating the complex factors, and adopting a holistic approach to analysis. The research gives decision-makers additional information with which to build sounder plans and reduce risks in the competitive energy market. From a scholarly perspective, this work contributes to the creation of thorough models and theories that reflect the complex interdependencies causing the volatility in gas prices. While recognizing the research’s inherent limitations, the study also points out areas that need additional study, paving the way for a better comprehension of gas price dynamics and advancing the body of knowledge in this field more generally.
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