Changes In Energy Intensity Research Articles

Possibilities of Changes in Energy Intensity of Production Depending on the Scale of Farm Investments in a Polish Region

The purpose of this paper was to analyze the possibility of changes in energy intensity of production in the context of farm investment scale. The empirical section relies on unpublished FADN microdata. The study answers the question of whether investments and fixed capital growth can contribute to improvements in environmental performance of agricultural production. As it turns out, the group of farms with the greatest amount of investments saw an increase in energy consumption costs, though at a much slower rate than growth in production value. As a result, there was a drop in energy intensity of production, defined as the ratio between energy costs and production value. These findings corroborate the hypothesis advanced in this study, namely that upon reaching a sufficiently large amount of investments, farms can become capable of reducing the energy intensity of their production activity. Hence, higher levels of capital productivity are attained when the farms invest in more energy-efficient fixed assets. Conclusions cannot be universal, as the analysis involved a portion of selected farms. However, the research indicates a trend in the study of energy intensity of production depending on the scale of investment and the validity of the investment comprehensiveness approach. These conclusions provide a basis for recommendations for an agricultural policy which should include support for investments that are large and comprehensive (in relation to the farm’s assets) and go beyond the simple renewal of assets. Therefore, in its investment-oriented measures, the agricultural policy should take the innovativeness (including energy efficiency) criterion into account.

The Potential of Using Artificial Intelligence (AI) to Analyse the Impact of Construction Industry on the Carbon Footprint

AbstractConstruction is an important sector of human activity that significantly impacts the environment. The impact of this sector can be analysed from different perspectives, such as consumption of natural resources, waste generation, energy intensity, and environmental change. The sector is increasingly promoting using renewable materials, energy-efficient practices, and planning those respects ecological processes and biodiversity. Against this background, it is important to take coordinated action across the sector and move to net-zero carbon standards through immediate action to raise awareness, implement innovation, and improve carbon management and reporting processes. Tools supporting the reduction of the adverse impacts of construction activities include artificial intelligence tools. The construction industry has long been considered a conservative and traditional industry but is now experiencing a technological revolution. Gradually, artificial intelligence (AI) principles and tools are beginning to be integrated into the various lifecycle processes of construction projects. This paper analyses the AI tools used to analyse carbon footprinting in the construction sector in terms of selected functionalities. The results of the research will form the basis for the development of a strategic plan for the development of AI within the research activities at the Faculty of Civil Engineering in Košice.

Effects of globalization and financial inclusion on energy intensity: The case of emerging economies

This research examines the effects of globalization and financial inclusion on the energy intensity of selected sixteen emerging economies by controlling the effects of economic growth, inflation, and stock market capitalization. The study period covers twenty-two years, starting from 2000 and ending in 2021, providing a complete perspective on the changing dynamics inside these countries. The study employs the novel technique of Method of Moment Quantile Regression (MMQR) to empirically assess the interlinkages between the employed factors on energy intensity. The findings suggest that when economies expand, engage in global trade, and encounter inflationary pressures, they tend to exhibit remarkable changes in energy intensity. The results find negative effects of globalization, GDP, and inflation on energy intensity. In contrast, a positive correlation exists between financial inclusion, stock market capitalization, and energy intensity. Policies must be designed with the objective of harnessing the advantages of globalization in order to enhance energy efficiency, encompassing elements such as technology transfer and regulatory harmonization. Concurrently, it is imperative to establish a sound relationship between financial inclusion and the expansion of stock markets, while also integrating sustainable energy practices.

Dynamic environmental quality effect of nuclear energy intensity, structural changes, and natural resources in Pakistan: testing load capacity factor hypothesis evidence

AbstractWith both electricity and clean energy cooking accessible to 40 million and over 100 million people respectively, Pakistan’s ecological challenges could persist as long as the energy-related issues remained unsolved. This is the motivation for examining the drivers of the country’s biocapacity and ecological footprint vis-a-vis load capacity factor (LCF) from the perspective of nuclear energy intensity, natural resources, structural change, and economic growth. By using the recently developed simulation of autoregressive distributed lag for dataset that covers 1971 to 2021, this investigation found that nuclear energy intensification and structural change both improves environmental quality by increasing the country’s ratio of biocapacity against its ecological footprint in the long run. Specifically, nuclear energy intensity and structural change have respective elasticities of 0.02 and 0.34 with LCF. With the country’s nuclear energy supply far below the natural gas, oil, and biofuels and waste sources, the country might as well be encouraged to increase the development of nuclear energy in tackling the persistent environmental woes. Contrarily, the investigation established that natural resources in the country is detrimental to environmental quality but only in the short run because a percent increase in natural resources is responsible for ~ 0.035 percent decline in LCF. Importantly, an inverted U-shaped relationship ensued between economic growth and LCF but only statistically significant in the long-run i.e. invalidating LCF hypothesis, thus suggesting an undesirable environmental consequence of economic prosperity. As a policy, and given the novel perspectives of nuclear energy intensity and structural change dynamics, these results incentivize Pakistan’s nuclear energy development drive and among among other environmental and economic policy initiatives. Graphical abstract

Identification of carbon responsibility factors based on energy consumption from 2005 to 2020 in China

In the process of rapid economic development, the relationship between energy consumption and economic development is close and the coupling relationship is complex. The study proposes a method for the assessment of integrated energy consumption factors. In particular, production and consumption driving factors are expressed in relation to the growth of primary energy consumption responsibility in the energy supply chain system. Specifically, based on previous research, the study builds a LMDI decomposition model based on the primary energy consumption responsibility - embodied energy (PECREE) formula. By analyzing the impact of changes in the final commodity quantity, value-added allocation factor, energy intensity, end-use energy consumption structure and primary energy consumption responsibility conversion factor on primary energy consumption responsibility, the study systematically reveals the linkage relationship between product production and consumption on energy consumption changes, and reveals the related driving mechanism of primary energy consumption responsibility growth. We choose China's energy supply system from 2005 to 2020 as a case study, focusing on the impact of changes in economic sectors on primary energy consumption responsibility, and provide a supplementary explanation from the commodity consumption side. The results show that the change of final use quantity is the most important factor causing the increase of primary energy consumption responsibility, but the impact is decreasing year by year. The impact of energy intensity change on primary energy consumption responsibility is mainly dominated by economic sectors with high energy consumption and high energy intensity. The impact of value-added allocation factor requires systematic consideration of energy intensity of economic sectors. The increase of the proportion of electricity in the end-use energy consumption structure of the economic sector promotes the growth of its primary energy consumption responsibility. The reduction of primary energy consumption responsibility conversion factor for electricity is one of the factors that inhibits the growth of primary energy consumption responsibility. Finally, we believe that while meeting the needs of social development and production and life of the people, China is already on the road of adjustment and transformation of its industrial structure and energy structure, and will continue to make progress in the future.

Research and development factor mismatch, production losses, and energy efficiency: A study on Chinese regional economies

In the face of global economic development and increasing energy demands, the efficient allocation of Research and Development (R&D) resources emerges as a universal concern. This study illuminates the complex relationship between R&D factor mismatch, production losses, and changes in regional energy intensity. Utilizing factor mismatch accounting framework and the STIRPAT model, we systematically assess the impact of spatial mismatches in R&D factors on R&D output and regional energy intensity. The analysis unfolds in three steps, first, the estimation of the degree of spatial R&D factor mismatch and the resulting output gap; second, the calculation of R&D output loss as a function of spatial mismatch coefficient and output share per location; and third, the examination of changes in energy intensity induced by R&D factor mismatch in terms of Total Factor Productivity (TFP), factor mismatch, and output structure. Our findings reveal that R&D factor mismatches can hinder energy intensity reduction, and that regional disparities in the process of factor market marketization significantly contribute to variations in regional energy intensity. These insights emphasize the critical role of efficient R&D resource allocation in promoting energy conservation and productivity, providing substantial implications for policy-making in sustainable growth and energy efficiency across diverse economies.

Emission-side drivers affecting carbon neutrality based on vegetation carbon sequestration: Evidence from China

To address climate change, the world needs deep decarbonization to achieve carbon neutrality (CN), which implies net-zero human-caused CO2 emissions in the atmosphere. This study used emission-side drivers, including socioeconomic and net primary productivity (NPP)-based factors, to determine the changes in CN based on vegetation carbon sequestration in the case of China during 2001–2015. Spatial exploratory analysis as well as the combined use of production-theoretical decomposition analysis (PDA) and an econometric model were also utilized. We showed that CN was significantly spatially correlated over the study period; Yunnan, Heilongjiang, and Jilin presented positive spatial autocorrelations, whereas Guizhou showed a negative spatial autocorrelation. More than half of CN declined over the period during which potential energy intensity (PEIE) and energy usage technological change were the largest negative and positive drivers for increasing CN. PEIE played a significantly negative role in increasing CN. We advise policymakers to focus more on emission-side drivers (e.g., energy intensity) in addition to strengthening NPP management to achieve CN.

Analysis of energy policy reform in Iran: Energy and emission intensity changes

The Subsidized energy system of Iran, with its high financial burden, failed to achieve its intended economic goals, resulting in increased energy consumption and pollutant emissions. Energy product subsidies in Iran are among the highest in the world and have important implications for developing countries. This study examines the impact of subsidy removal policy on energy and emission intensities using a Dynamic Computable General Equilibrium model. The findings reveal that after subsidy removal, total output experiences an annual growth of around 1.5%, with output composition changes mainly favoring agriculture and services that are less energy dependent. However, prices soar by at least 10% owing to the higher costs of energy products. The decreasing trend in energy intensity after subsidy reform is accompanied by a decrease in emission intensity. Higher prices of energy after subsidy removal and the corresponding improvement in the efficiency of energy consumption lead to lower emission intensity.

Forecasting China’s CO2 emissions and identifying key drivers: an application of the improved RFAGM model and LMDI decomposition methods

ABSTRACT China, the world’s largest CO2 emitter, has pledged to reduce its carbon intensity by 18% by 2025, which requires accurate forecasting of its emissions and their drivers. However, existing gray models have limitations in dealing with fluctuating data or long-time series data, and they often suffer from overfitting and poor generalization ability. Moreover, there is a lack of research and judgment on the future changes of emission drivers. To address these issues, this study proposes a fractional order gray adaptive rolling model (RFAGM(1,1)) that optimizes the background generation and incorporates the rolling mechanism. We apply RFAGM(1,1) to forecast China’s emissions, GDP, population, and consumption of raw coal, crude oil, and natural gas from 2020 to 2025. Our results show that RFAGM(1,1) achieves significantly higher accuracy than standard gray models, except for population. The projections indicate that China will meet the 18% carbon intensity reduction target by 2025. Furthermore, the LMDI decomposition reveals that economic growth and population changes have positive cumulative impacts on emission growth (245.68% and 11.95%, respectively), while energy intensity and structural changes have negative cumulative impacts (−151.60% and −6.02%, respectively). The improved forecast enables the evaluation of climate policies, while the factor analysis provides valuable insights for developing evidence-based strategies to achieve China’s carbon peaking and neutrality goals by 2030/2060.

System dynamics prediction and development path optimization of regional carbon emissions: A case study of Tianjin

The prediction of regional carbon emissions and the optimization of development paths are particularly important. Regional heterogeneity leads to different links between carbon emissions and population, economy, industry, energy, environment, and other factors. However, most of the previous studies carried out the accounting, factor decomposition, prediction, and decision-making of regional carbon emissions alone, which failed to form the sustainable assessment of regional carbon emissions. Therefore, the carbon emissions coefficient, Logarithmic Mean Divisia Index, system dynamics model and Interlink Decision Making Index were selected in this study to establish the regional carbon emissions systems framework. In this study, a mega-city such as Tianjin was taken as an example, the comprehensive assessment and prediction of regional carbon emissions system was carried out. The results show the change in energy intensity had the strongest mitigation effect, reducing carbon emissions by 106.17 million tons in total, and the per capita GDP effect had the strongest promotion effect, with a cumulative contribution of 265.19 million tons of carbon emissions. Among all 13 scenarios in Tianjin, Scenario-12 is identified as the optimal development path and provides policy suggestions. The results of this study not only verify the effectiveness and necessity of the framework, but also provide guidance tools for regional carbon emissions development. The results of this study validate the effectiveness and necessity of the framework to provide guidance for the regional carbon emissions development path. The results of the case also provide help for the optimization of Tianjin's carbon emissions development path.

Exploring the energy intensity – per capita income nexus: evidence from middle-income and high-Income countries

ABSTRACT Assessing changes in energy intensity has become increasingly important in recent years as economies strive toward greater environmental awareness and conservation while, at the same time, improving residents’ standard of living, through economic growth. This study explores the relationship between energy intensity and real per-capita income across the period 1981–2015 for 60 countries. We divide the sample into two segments: middle-income and high-income countries to better explore this relationship. The analysis uses a spline functional form which allows for greater flexibility to detect non-linear relationships. The results for middle-income countries show three distinct threshold levels of per capita income at which the direction of the relationship between energy intensity with per capita income changes. This suggests changes in energy intensity in middle-income countries occur due to changes in demand, fuel substitution and technological progress. The results with respect to the high-income countries do not indicate a threshold level with respect to their current level of income. This may be indicative that these countries have already passed their turning point prior to 1990 and are progressively reducing their energy intensity.

Import competition and firm energy intensity: Evidence from China

The impact of trade on environmental performance has recently become a hot topic. However, little is known about whether import trade affects the energy use of developing countries. Therefore, we use detailed micro-firm data on China’s manufacturing sector to investigate the impact of import competition on firms’ coal energy intensity. We measure the degree of import competition by import penetration and estimate firm-level data for 2001–2010 using a fixed effects model and 2SLS strategy. The findings suggest that import competition significantly reduces coal energy intensity for Chinese firms, which implies that increasing imports will make it easier for China to meet the objective of “carbon peaking, carbon neutral”. By decomposing import competition, we find that differences in the sources of import competition can explain decline changes in firm energy intensity. The theoretical framework and mechanism tests show that the improvement of production and technology application efficiency are the main channels through which import competition decreases energy intensity. However, there is no evidence that import competition has driven firm green transformation process. Finally, we constructed an industry correlation index using the input–output relationship between industries to confirm that import competition affects the energy intensity of near-neighbor industries.

Driving factors of changes in energy intensity: A comparison between energy exporting and importing countries

This paper compares the driving factors of changes in energy intensity in both net energy exporting and importing countries using a DEA-Malmquist (Data Envelopment Analysis) and panel GMM (Generalized Method of Moments) methods over the period of 2000–2021. The findings show that technological progress has played a significant role in reducing of energy intensity in both groups. Moreover, we use the DEA method to decompose the Malmquist total factor productivity (TFP) into its components including technical change (TC), efficiency change (EC), pure efficiency change (PEC) and scale efficiency change (SEC). The results show that in energy exporting countries, the effects of each of these TFP components on energy intensity are negative but relatively weak, while the effects of these components on reducing energy intensity in importing countries is considerable. Specifically, the estimated coefficient of the pure efficiency component in reducing energy intensity in very remarkable, which shows the high importance of the efficiency components of TFP in energy management. Next, we investigate what is the main driver of technological progress in both the energy exporting and importing countries. The findings imply that in net energy exporting countries trade openness is a dominant factor to improve productivity, while in net energy importing countries, internal R&D is the dominant factor for improving technological efficiency.

The Impact of Energy Intensity on Environmental Inequity

With the determination of carbon neutrality target, energy structure has become an important indicator to measure regional pollution emissions. In order to study the impact of energy intensity on environmental inequity, this paper uses threshold model to study the impact of energy intensity change on environmental inequity based on the panel data of 30 provinces from 2000 to 2020.The results show that there is a non-linear U-shaped relationship between energy intensity and environmental inequity. With the increase of energy intensity, the impact of energy intensity on environmental inequity does not play the role of mitigation, but more significantly aggravates the degree of environmental inequity.

Impact of Key Drivers on Energy Intensity and GHG Emissions in Manufacturing in the Baltic States

The improvement in energy efficiency (EE) and increasing consumption of renewable energy sources (RES) in manufacturing play an important role in pursuing sustainable development in the Baltic States and contribute to the transition to a low-carbon economy. This paper presents the results of a detailed analysis of the channel through which EE, along with structural activity changes, passes energy intensity and total energy savings and in combination with other key drivers results in reductions in greenhouse gas (GHG) emissions in manufacturing in Estonia, Latvia, and Lithuania during the period 2010–2020, taking into account the role of transformations in the energy and climate framework of the European Union (EU). The Fisher Ideal Index, the Kaya identity, the Logarithmic Mean Divisia Index (LMDI), and comparative analysis methods are used. The results of the impact analysis of key drivers on energy intensity showed different contributions towards improvements in EE and structural activity changes to changes in energy intensity in manufacturing, which decreased by 53.1% in Estonia, by 30.5% in Lithuania, and by 16.5% in Latvia. The dominant role of EE improvements on total energy savings is identified. The results of the GHG decomposition analysis showed that because of improvements in energy intensity, reductions in the share of fossil fuels, and increases in labour productivity, number of employees, and emissions intensity, the GHG emissions decreased by 35.5% in Estonia, 40.4% in Latvia, and 8.1% in Lithuania. The results confirm the need for new policies and the implementation of relevant commitments to save energy and increase the contribution of RES in all three countries.

Unfolding the painting scroll of energy intensity changes in Chinese mainland (1990–2019): A regional perspective

Improving the aggregate energy efficiency is consequential for China's sustainable development and carbon dioxide reduction, but there lacks a systematic analysis of China's energy intensity long-term changes. China's energy intensity fell sharply by 74.7% with great heterogeneity across regions during the years 1990–2019, but rose steadily by 7% from 2001 to 2005. This paper combined decomposition approach and econometric analysis to unfold the painting scroll of energy intensity changes in Chinese mainland (1990–2019) from a regional perspective. Decreased by the increase of relative energy price, the expansion of technology transactions and the surrounding provinces' efforts, provincial intensity was found as the main driving force of the aggregate energy intensity reduction in general from 1990 to 2019. Yet, after controlling the effects of these factors, macroeconomic cyclical fluctuation, triggered by Chinese reindustrialization process, increased the provincial intensity, hence amplified the aggregate energy intensity between 2001 and 2005. The results of this study will provide sufficient basis for the policy implications on the improvement of energy efficiency in China and developing countries in the future.

Homeostasis of carbohydrates and reactive oxygen species is critically changed in the brain of middle-aged mice: Molecular mechanisms and functional reasons

The brain is an organ that consumes a lot of energy. In the brain, energy is required for synaptic transmission, numerous biosynthetic processes and axonal transport in neurons, and for many supportive functions of glial cells. The main source of energy in the brain is glucose and to a lesser extent lactate and ketone bodies. ATP is formed at glucose catabolism via glycolysis and oxidative phosphorylation in mitochondrial electron transport chain (ETC) within mitochondria being the main source of ATP. With age, brain's energy metabolism is disturbed, involving a decrease in glycolysis and mitochondrial dysfunction. The latter is accompanied by intensified generation of reactive oxygen species (ROS) in ETC leading to oxidative stress. Recently, we have found that crucial changes in energy metabolism and intensity of oxidative stress in the mouse brain occur in middle age with minor progression in old age. In this review, we analyze the metabolic changes and functional causes that lead to these changes in the aging brain.

From Targets to Action: Analyzing the Viability of REPowerEU in Achieving Energy Sustainability

EU’s energy sector is facing turbulent times as it strives to strengthen energy independence without losing sight of providing affordable and sustainable energy to all. The European Commission’s REPowerEU plan to accelerate the EU’s energy transition places additional pressure on each member state’s path to energy sustainability. To reach this objective, policymakers must assess the present energy sustainability levels of each member state, identify areas for development, and monitor the county’s progress over time. The purpose of this article is to analyze and compare the energy sustainability levels of the EU member states using a variety of indicators and to identify key cornerstones for advancing their energy transition. This study develops an energy sustainability composite index (ESCI) in order to unravel and compare the multiple lawyers of energy sustainability, including energy security, primary energy intensity, share of renewable energy resources, energy efficiency, CO2 emission intensity, and energy poverty. Log-Mean Divisia Index (LMDI) decomposition analysis is utilized to track the progress of energy policy in achieving reductions in energy-related CO2 emissions from 2015 to 2019. Changes in CO2 emissions were decomposed using Kaya identity factors to determine which of the following factors contributed the most to the changes: changes in emission intensity, energy intensity, economic or population growth. The results indicate that all EU member states have untapped potential for improving energy sustainability.

Assessing Energy Consumption and Energy Intensity Changes in Pakistan: An Application of Complete Decomposition Model

Complete decomposition model has been employed in the present study to decompose the changes in energy consumption and energy intensity in Pakistan during 1960 to 1998. A general decomposition model raises a problem due to residual term. In some models the residual term is omitted, which causes a large estimation error, while in some models the residual term is regarded as an interaction that might create a puzzle for the analysis. A complete decomposition model is used here to solve this problem.

Probing CO2 emission in Chengdu based on STRIPAT model and Tapio decoupling

Carbon dioxide (CO2) is the primary driver of global warming. Conducting CO2 emission projections and identifying the decoupling relationship between CO2 emission and economic growth are significant guidelines for assessing carbon reduction measures and green economic development. This study accounts for Chengdu's CO2 emission from 2009-2020 in terms of energy activities, industrial processes, waste disposal, and photosynthetic in green spaces, then follows the STIRPAT model to project CO2 emission for the four scenarios from 2021-2035 and uses the Tapio decoupling model to analyze the CO2 emission and economic growth decoupling status. The results show that: (1) The increase in energy consumption is the leading cause of the growth of CO2 emission in Chengdu. (2) Population and energy intensity will be the main influencing factors of Chengdu's CO2 emission. 1% change in population and energy intensity will cause 1.617128%, and 1.355371% changes in CO2 emission; (3) Chengdu can meet its commitment to peak carbon by 2025. Business as usual (BAU) is the best choice for Chengdu to achieve low-carbon development. At the peak, the CO2 emission will be 66.04 million tons, and the per capita CO2 emission will be 3.15 tons. (4) During the outlook period, Chengdu's CO2 emission and economic growth will change from weak decoupling to strong decoupling, reaching an ideal state. Measures focused on stimulating more low-carbon potential and momentum, strengthening the green transformation of all aspects of production and life can help Chengdu achieve a more excellent carbon peak. The results greatly reference for the carbon peaking process in other cities worldwide.