Analysis of Energy Consumption Patterns and Climate Effects Using Panel Data

This study aims to determine Central Java population welfare dynamics by looking at the pattern of expenditure and the nutritional and food consumption level. The data used is data on the expenditure and consumption of the Central Java population in 2016–2020. The data analysis method used is descriptive analytical. The study of expenditure patterns shows that during the period 2016–2020, the Central Java population welfare is increasing. Still, the population of rural areas is less prosperous than the population of urban areas. The welfare increase is also shown by decreasing the cereals group consumption. The analysis of the nutritional consumption level indicates that the energy and protein consumption level tend to increase during 2016–2020. The level of energy consumption is categorized as sufficient, and protein consumption level is categorized as good. In rural areas, energy consumption and energy consumption are higher than in urban areas; conversely, protein consumption and protein consumption in urban areas are higher than in rural areas.

This study analyzes the impact of energy security and geopolitical risks on energy consumption in countries with geopolitical risk from 1985 to 2021, with the aid of financial development, economic growth, and human capital as moderating variables. Employing robust panel data econometric techniques, the analysis reveals that heightened geopolitical risks and compromised energy security significantly affect energy consumption patterns. The increase in energy security risk by 1% has a dual impact on energy consumption patterns in countries with high geopolitical risk, as renewable energy consumption increases by 0.1227%, while non-renewable energy consumption also increases by 0.113%. As geopolitical risk increases by 1%, energy consumption patterns change, decreasing renewable and non-renewable energy consumption. Specifically, renewable energy consumption decreases by 0.00010% with a 1% rise in geopolitical risk. A 1% increase in financial development significantly impacts energy consumption in these countries. The adoption of sustainable energy solutions is positively correlated with the development of financial markets in high-risk countries, with non-renewable energy consumption decreasing by 0.0187% as financial development progresses. Additionally, an increase in economic growth of 1% leads to a 0.6750% increase in renewable energy consumption, indicating a preference for renewable sources as economies expand, aligning with global sustainability efforts. The findings underscore the importance of resilient energy policies considering geopolitical uncertainties while promoting sustainable economic development. This study offers critical insights for policymakers aiming to balance energy security with financial stability in geopolitically sensitive regions, providing a comprehensive framework to guide strategic decisions in the face of evolving global risks.

The COVID-19 pandemic has generated multiple impacts. In particular, in the educational sector, the virtual class modality generated changes in the patterns of energy consumption at the institutional level; the identification of this consumption will allow us to reflect on new energy saving and efficient use strategies. In this research, we present a case study of the effects of the COVID-19 pandemic on electricity consumption in 13 state universities in Michoacán, Mexico. Electric energy consumption has been evaluated before and during the presence of the COVID-19 between 2019 and 2020. The comparative analysis estimated the reduction in energy consumption and its economic and environmental impact. The results show a considerable decrease in electricity consumption, generating an average saving of 76.24 MWh/month, which translates into an annual emission reduction from 2019 to 2020 of approximately 497 TnCO2e, and in economic terms of $8,882.25 USD each month. In general, it was identified that consumption patterns in the use of machinery and computer equipment for administrative activities were drastically reduced. If education continues in virtual or hybrid modes, energy consumption schemes will continue to decline and institutions could move towards resilient, affordable, and sustainable models of energy production and consumption.

The importance of energy consumption and environmental protection is one of the key issues of the past decade. Most of the energy consumption during the summer season in residential buildings, offices, hospitals, and hotels is related to their chiller plant. The highly variable cooling demand of the buildings connected to a hybrid chiller plant has to be distributed among absorption and vapor compression chillers to achieve higher energy efficiencies. Cooling load sharing strategies in chiller plants have a significant impact on energy consumption and consequently with more productivity and environmentally protected. This paper examines the behavior and pattern of energy consumption in a hybrid chiller plant that includes a combination of vapor compression and absorption chillers. In order to properly understanding the pattern of energy consumption, an existing mechanical room for a hospital in Tehran has been studied for six months and its energy consumption has been compared with the software modeling. This comparison includes the capacity of each chiller, the amount of energy consumed by vapor compression chillers and absorption chillers, and the percentage of their cooling energy allocation. The results indicate that the sequence of the chiller function and how they are placed in the circuit during a partial load is in the highest importance in viewpoint of energy saving.

The energy scene in the twentieth century both has been and will continue to be characterised by change. Thus, there have been fundamental changes in both the level and pattern of world energy production and consumption. New sources of energy have become commercially viable — for example, electricity from nuclear power stations — while other sources have increasingly encountered supply constraints, for example hydro-electricity in many developed countries. The consumption of both oil and natural gas has increased many-fold, and one of the principal concerns of energy policy in the late 1970s is the forecast of future shortages of oil.

This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems, which face challenges due to their high population density. The proposed algorithm integrates the strengths of machine learning techniques, more specifically, the long short-term memory (LSTM) technique, with teaching learning-based optimization algorithms. To achieve optimization, the algorithm learns from historical data on energy consumption and communication patterns specific to the modeled system. By leveraging these insights, it can predict future energy consumption and communication patterns accurately. Additionally, the algorithm incorporates a modified teaching learning-based optimization approach inspired by the teaching and learning process in classrooms. It adjusts the system parameters based on feedback received from the system, thereby optimizing both energy consumption and communication systems. The effectiveness of the proposed algorithm is evaluated through a case study conducted on the test system, where historical data on energy consumption and communication patterns are analyzed. The results demonstrate that the algorithm efficiently optimizes the communication and energy management systems, leading to substantial energy savings and improved communication efficiency within the test system. In conclusion, this study presents a hybrid machine learning and modified teaching learning-based optimization algorithm that effectively addresses the communication and energy management challenges in the test system. Moreover, this algorithm holds the potential for application in various smart city domains beyond the test system. The findings of this research contribute to the advancement of smart city technologies and offer valuable insights into reducing energy consumption in densely populated urban areas.

Rapid Economic Growth at the Cost of Environment Degradation? - Panel Data Evidence from BRIC Economies

The high quality of life in the modern sense is directly related to energy consumption and associated with the provision of «clean» food products and environment, comfortable housing, public and private transport. Increasing the availability of energy for the developing countries allows their residents to live longer and more comfortably. At the same time, in the developed European countries with high quality of life, energy consumption is decreasing due to the implementation of energy efficiency and energy saving policies. The Human Development Index, the world’s best-known and most widely used integrated assessment of quality of life, does not include energy consumption indicators. The aim of our research is to study the relationship between energy consumption and quality of life, and prove the need to consider energy consumption indicators in order to improve the methodology framework for assessing quality of life. Using the method of cluster analysis, 77 countries of the world are grouped according to a set of indicators that characterize income, energy consumption, use of renewable energy sources, and CO2 emissions. As a result, the relationship between the level of human development, which is a universal characteristic of quality of life, and these indicators was identified and evaluated. It has been proved that the most prosperous countries in terms of quality of life and energy use are those in which relatively low indicators of primary energy consumption and CO2 emission are combined with high incomes and human development level. The progressive structure of energy consumption ensures the achievement of a higher quality of living, while high energy consumption is not a sufficient condition for this. Against the background of low levels of total primary energy consumption and GDP per capita, CO2 emissions, as well as a low share of renewable energy in total energy consumption, high quality of life is present mainly in the «new» EU member states, which provide it through rational energy consumption. The average level of human development is inherent in a group of countries of the former USSR (which includes Ukraine), as well as some countries in Latin America, the Middle East and Southeast Asia. At the same time, countries such as China and South Korea are reducing energy consumption owing to technological progress and have a positive dynamics of human development indicators. Instead, low levels of energy consumption in the former Soviet Union and Latin America are due to insufficient sustainability of economic and social development, human development in particular. The obtained results substantiate the need to improve the methodology for assessing the quality of life taking into consideration energy consumption indicators.

Ngawi is the sixth paddy producer in Indonesia and certainly has an influence on the food security of paddy farm households. This study aimed to analyze the proportion of food expenditure (PFE), energy and protein consumption, the relationship between food expenditure and energy consumption, and the condition of food security. The basic method used descriptive analytic. The number of respondents based on the slovin formula was 87 households. The sample selection used stratified random sampling. The data analysis method used correlation with SPSS 22 and cross indicator between PFE and energy consumption level. The results showed that the average of PFE was 58.81%. The average energy consumption was 4,272.2 kcal/household/day with an energy consumption level of 81.93% while the average protein consumption was 122.1 grams/household/day lower than the average household RDA consumption. Food expenditure had a significant relationship to energy consumption with a correlation coefficient of 0.925 including a very strong and unidirectional relationship. The contribution of household food security conditions were 28.7% secure; 32.2% vulnerable; 10.3% less secure; 28.8% insecure.

Due to high level of energy consumption and increasing environmental concerns, energy efficiency has become a critical issue today. Buildings alone account for around 30 percent of the world’s total energy consumption. The way buildings are designed and constructed today will not only have an impact on their operating costs, but it will also affect the world’s energy consumption patterns and environmental conditions for many years to come. For much of the building industry in Thailand, the designed-in approach to energy-efficient design does not reflect current market practice. In reality, without passive design, numerous opportunitiesfor designing better performance buildings can be wasted.The integration of passive design is thus a key to energy conscious buildings. The integration of passive design approach optimizes the interactions between the natural environment, building envelope and systems as an integrated system. This research examines which components work best altogether to save energy and reduce environmental impacts on buildings in the tropical region. The outcomes of this research aim to set up the criteria of passive and low energy in building design for the tropical climate in Thailand. These fundamental differences will lead to a very different architectural and constructional design. It is imperative that the decision be made at an early stage in the design and there are tremendous opportunities to use smart, energy efficient designs to reduce the energy footprint of the built environment for decades to come.

With the fast and rapid pace of developments in wireless technology, energy consumption has become a problem of great significance for future networks. Over the past few years, several energy monitoring policies have been initiated to promote energy efficiency emphasizing the active and important role of consumers to realize this goal. This study resolves the energy consumption issues of broadband networks and determines the energy usage associated with high spectrum allocation in future broadband networks by leveraging clustering from the data mining domain. For analyzing the overall patterns of energy consumption in broadband networks, this study segments the broadband networks based on the similarities of their electrical load profiles and the proportion of energy usage per hour (%) as a common framework and divides the users into different groups. The prime objective for the segmentation is to provide personalized recommendations to each group to reduce the energy consumption and associated costs thereby fostering energy efficiency measures and improving consumer engagement. The segmentation is obtained by an iterative process based on computational clusters calculation which is finalized by a post clustering analysis. Post clustering analysis involves visualization and statistical data mining techniques to analyze the energy consumption patterns and reallocating to a more appropriate group. The K-Means clustering technique is utilized for this purpose which provides the best prediction accuracy of 98.46% for energy load profiles at the spectrum of 100GHz. The energy consumption segmentation of the consumers provides knowledge and a better understanding of the consumer for optimizing energy consumption for future broadband.

The ongoing digitization of the economy has led to the creation and functioning of platform model socio-economic systems. It is also reflected in the changes in patterns of energy consumption in households. In the first cross-section, it is an industrial revolution, with environmental benefits. However, platforms are primarily a revolution in the consumption sphere, and here, the effects of digitization are not fully recognized. Our social needs are increasingly met “through accessibility” without us leaving our home. Due to the home’s multifunctionality, based on the availability of platform services, household energy consumption should be viewed differently today than before. The article aims to show the changes in Central and Eastern Europe (CEE) household energy consumption between 2008–2018 and their assessment through the prism of the economy’s platformization methods. The study presents the changes in energy consumption in households and determines the correlations between platformization (the author’s index) and changes in energy consumption in households with the use of taxonomic methods. The platformization leaders—Estonia and Lithuania—were subjected to a more detailed analysis. The presented method(s) may be useful in predicting the changes in households’ energy consumption caused by the digitization of other countries in the region (countries under transformation and outsiders-Bulgaria, Romania), in implementing household energy management systems, and in a better adjustment of regulations directed at these consumers.

Per-user pricing is possible with cloud computing, a relatively new technology. It provides remote testing and commissioning services through the web, and it utilizes virtualization to make available computing resources. In order to host and store firm data, cloud computing relies on data centers. Data centers are made up of networked computers, cables, power supplies, and other components. Cloud data centers have always had to prioritise high performance over energy efficiency. The biggest obstacle is finding a happy medium between system performance and energy consumption, namely, lowering energy use without compromising system performance or service quality. These results were obtained using the PlanetLab dataset. In order to implement the strategy we recommend, it is crucial to get a complete picture of how energy is being consumed in the cloud. Using proper optimization criteria and guided by energy consumption models, this article offers the Capsule Significance Level of Energy Consumption (CSLEC) pattern, which demonstrates how to conserve more energy in cloud data centers. Capsule optimization's prediction phase F1-score of 96.7 percent and 97 percent data accuracy allow for more precise projections of future value.

Non-renewable energy fosters economic growth, whilst simultaneously it serves as the primary source of carbon dioxide (CO 2 ) emissions. Using data from the world's three major carbon emitters over the period 1980–2020, this paper adopts the non-linear autoregressive distributed lag (NARDL) approach to explore the dynamic nexus between energy consumption, economic growth and CO 2 emissions. The results reveal that energy consumption matters for economic growth. From a short-term perspective, an increase in oil consumption positively impacts economic growth in the United States and India, whereas a negative shock solely affects China's economic growth. Regarding coal consumption, a positive shock only affects the US's economic growth, while China and India are more likely to be affected by a negative consumption shock. In the long run, the nexus between oil consumption and economic growth exhibits a similar asymmetric pattern in China and the United States. Concerning the effect of coal consumption on economic growth, only negative shocks count for China and positive shocks for the United States. The findings also identify various patterns of energy consumption on CO 2 emissions. An increase in oil consumption, whether from a short- or long-run perspective, increases CO 2 emissions in all three countries, while a decrease in oil consumption only mitigates CO 2 emissions in China and the United States. There are more complicated patterns to the role of coal consumption in CO 2 emissions. The results vary significantly from country to country; however, energy consumption has generally promoted economic growth as well as resulted in environmental issues that cannot be disregarded.

A survey focusing on energy consumption in rural households in three typical areas of China was conducted in the form of questionnaire. Nearly 800 households were sampled in the survey. The purpose of the survey was to find out some unreasonable energy consumptions and offer some practical solutions. The results indicate that: firstly, in rural areas, energy consumption level is quite low, meanwhile, the non-commercial energy consumption accounts for a large percent, and this even occupies more than 90 % in Pu’er; secondly, the use ratio of solar energy in rural households is very low and has no correlation with availability of local solar energy resources, for example in Xinzhou, the use rate of solar water heating is less than 5 %; thirdly, in rural areas of Xinzhou, household energy consumption per capita is 1.24 times of the national rural average, and it is less than the average in Jingzhou and Pu’er. Fourthly, the use ratio of energy-saving lamp is lower than 50 % in all three areas; finally, there is a certain correlation between income level and commercial energy consumption, and the correlation coefficient for each area is 0.36697, 0.29312, and 0.45456, respectively.