Changes In Energy Intensity Research Articles

Factors driving aggregate service sector energy intensities in Asia and Eastern Europe: A LMDI analysis

The economywide energy intensities in the service sectors are declining in many countries worldwide. We identify the drivers of the declining trends by employing the Logarithmic Mean Divisia Index (LMDI) on annual data from 16 countries in the Asia and Eastern Europe for the 2000–2014 period. We find that the change in fuel mix has little contribution to driving the economywide energy intensity of the service sector down during the study horizon. Instead, the change in energy intensity contributes to a decrease in economywide energy intensity of service sectors in most countries except the Czech Republic, Estonia, Latvia, and Turkey. Moreover, since energy intensity is inseparable from economic development, changes in economic structure are an essential determinant of the economywide energy intensity of service sectors. This work also analyzes the sectoral attribution and energy feedstocks attribution of economywide energy intensity of the service sectors.

Tracking Key Industrial Sectors for CO2 Mitigation through the Driving Effects: An Attribution Analysis.

The heavy pressure to improve CO2 emission control in industry requires the identification of key sub-sectors and the clarification of how they mitigate CO2 emissions through various actions. Focusing on 30 Chinese provincial regions, this study quantifies the contribution of each industrial sector to regional CO2 mitigation by combining the logarithmic mean Divisia index with attribution analysis and extract the key sectors of CO2 mitigation for each region. Results indicate that during 2010-2019, significant emission reduction was achieved through energy intensity (74%) in Beijing, while emission reductions were attained through industrial structure changes for Anhui (50%), Henan (45%), and Chongqing (45%). The contribution to emission reduction through energy structures is not significant. The production and supply of power and heat (PSPH) is a central factor in CO2 mitigation through all three inhibitive factors. Petroleum processing and coking (PPC) generally contributes to emission reduction through energy structures, while the smelting and pressing of ferrous metals (SPMF) through changes in industrial structures and energy intensity. PSPH and SPMF, in most regions, have not achieved the emission peak. Except in the case of coal mining and dressing (CMD), CO2 emissions in other key sectors have almost been decoupled from industrial development. CMD effectively promotes CO2 mitigation in Anhui, Henan, and Hunan, with larger contribution of PPC in Tianjin, Xinjiang, Heilongjiang, and that of smelting and pressing of nonferrous metals in Yunnan and Guangxi. The findings help to better identify key sectors across regions that can mitigate CO2 emissions, while analyzing the critical emission characteristics of these sectors, which can provide references to formulating region- and sector-specific CO2 mitigation measures for regions at different levels of development.

Quantitative decomposition of China's industrial energy consumption structure based on production-theoretical decomposition method

The environment constraints have become more restrictive over time, because the transformation of energy consumption structure (ECS) is the key path to achieve the commitment of net-zero emission. However, the adjustment of ECS involves the influence of many factors, including economic factors, energy factors and social factors. Not to mention how ECS-driving factors under tougher environmental regulation may behave throughout long time period remains explored. According to the background, the periodic driving factors of industrial ECS in China based on the periodic effect of environmental regulation on ECS are discussed. In this paper, industries of China are divided into four sectors from 1998 to 2017, and a multi-layer LMDI decomposition model is constructed to decompose ECS of the four sectors in stages. Since the industrial and commercial sector provides the largest contribution rate, the LMDI model based on PDA production theory method is applied to decompose industrial and commercial ECS, aiming to deeply explore the main driving factors of ECS in subindustries, and formulate scientific energy and environmental policies. The results show that from the perspective of the whole industry, the per capita pollution control investment has the most significant negative contribution, and the economic activity effect provides a great positive contribution to the ECS. In terms of subindustries, the main negative drivers are potential energy intensity and GDP technology change.

The Nexus among Energy Intensity, Energy Mix and Economic Performance in Europe: A Decomposition Analysis

Using a versatile piecewise linear regression model, this study revisits the connection between energy intensity, energy mix, macroeconomic variables, and economic performance. The analysis determines a threshold effect of income growth on changes in energy intensity based on dynamic panel data for a collection of European economies from 1990 to 2020. The study builds on the three specifications: The Skelton model (M1), the human capital model (M2), and the policy model (M3). The Skelton model is a fundamental model that explains the energy economic relationship with energy use in many economic sectors and with various energies. Additionally, in M2, achieve basic and secondary education as human capital, investment, and commerce as models for policies (M3). Numerous diagnostic procedures are carried out to examine serial correlation, heteroscacity, and cross-sectional dependence. In order to evaluate the robustness of the results, the specification of the dynamic panel is put through the Generalized Method of Movement (GMM) regression framework. Although energy intensity and income growth are negatively correlated for the entire sample and study period, the rate of decline significantly slows after the level of per capita income reaches $5,000, by more than 30%. According to the analysis's findings based on index decomposition, structural change is crucial for intensity levels across all nations. Additionally, moving from fossil fuels to renewable energy sources has a positive correlation with economic success in terms of energy intensity. While increasing energy intensity spurs global growth because of the current state of environmentally friendly technological innovation.

Spatiotemporal variations and driving forces of per capita carbon emissions from energy consumption in China

The carbon emissions vary over space and time in China, as well as driving forces. It is particularly important to analyze the spatiotemporal variations and driving factors of China’s per capita carbon emissions. This study adopted global Moran’ I and local indicators of spatial association to analyze the spatial autocorrelation of per capita carbon emissions in China during 2004–2019 and discussed the driving factors of per capita carbon emissions by geographically and temporally weighted regression model. The results demonstrated a positive spatial correlation between inter-provincial per capita carbon emissions, but this correlation has gradually decreased since 2008. The High-High clusters were concentrated in the Bohai Economic Rim and the Low-Low clusters were mainly located in the south. Driving factors of per capita carbon emission at the provincial level have spatiotemporal heterogeneity. From 2004 to 2019, per capita GDP, urbanization rate, and energy intensity are the main contributors to per capita carbon emissions, and the role of per capita GDP is weakening, while urbanization rate and energy intensity change in the opposite direction. Foreign direct investment is the main disincentive in most regions. These findings provided a reference for emission reduction policies implemented in different regions.

Changes in Energy Consumption and Energy Intensity in EU Countries as a Result of the COVID-19 Pandemic by Sector and Area Economy

Energy is vital for the proper functioning of the various sectors of the economy and social life. During the pandemic, there have been some changes in these aspects that need to be investigated. The main objective of this article is to identify the direction of change caused by the COVID-19 pandemic in energy consumption and energy intensity in sectors and economic areas in EU countries. The specific objectives are to identify the importance of energy consumption in sectors and areas of the economy in individual EU countries; to determine the dynamics of change and variability during the pandemic in energy consumption in individual sectors and areas of the economy in EU countries, especially during the COVID-19 pandemic; to determine the changes in energy intensity of individual economic sectors and the differences in energy intensity between individual EU countries, including during the COVID-19 pandemic. Using a purposive selection method, all 27 EU Member States were selected for the study on 31 December 2020. The analysed period covered the years 2005–2020. The sources of material were literature and data from Eurostat. Descriptive, tabular and graphical methods, dynamic indicators with a fixed base and variable base, Gini coefficient, coefficient of variation, Pearson’s linear correlation coefficient, and multi-criteria analysis were used for analysis and presentation. It was found that the structure of energy consumption had remained unchanged for several years, with transport, industry and households dominating. There were no significant differences between countries. The COVID-19 pandemic reduced energy consumption in all sectors of the economy, the largest in transport and services and the smaller in industry. At the same time, household energy consumption increased. As a result of the pandemic, there was an increase in energy intensity in all sectors of the economy, the largest in industry. Western European countries had a lower energy intensity of the economy than Central and Eastern European countries. There was little change over several years. Countries generally maintained their ranking. The pandemic did not change anything in this respect, meaning that it had a similar impact on individual EU countries.

The Driving Forces of Energy-Related CO2 Emissions in the United States: A Decomposition Analysis

This paper uses the logarithmic mean Divisia index (LMDI) approach to decomposition analysis to identify the factors that influence changes in carbon dioxide (CO2) emissions in the United States.  The LMDI approach decomposes CO2 emissions into specific determinants.  The data set includes the 50 states plus the District of Columbia from 1998 – 2018.  The five factors that influence the change in CO2 emissions include the emissions per unit of fossil fuel consumption, share of fossil fuels in total energy consumption, energy intensity, GDP per capita, and population.  The results indicate that, during the 20-year period, CO2 emissions declined in 36 states plus the District of Columbia.  The reduction in energy intensity served as the most important factor in the change of CO2 emissions, offsetting 63 percent of the effects of per capita GDP and population.  From the perspective of climate change, the importance of a change in energy intensity demonstrates the effectiveness of a decrease in primary energy consumption per unit of GDP.   

Automation of processes at oil and gas enterprises as the main factor of resource saving

This article considers the practicality of applying automation processes for resource saving. For this purpose, the issue of energy intensity of the oil and gas complex was investigated. We also analyzed the life cycle of the oil and gas industry product, i.e. fuel. Then we studied how automation systems can be applied at its different stages and what changes in energy intensity of an enterprise the introduction of automated systems leads to.

Factor decomposition for global and national aggregate energy intensity change during 2000–2014

Intensity indicators are indispensable for monitoring global sustainability. Here aggregate energy intensity (AEI) was established to measure global sustainable energy resources utilization. Compared to traditional energy intensity, AEI integrates energy consumption and its concomitant economic gains. On this basis, temporal variation of global and regional AEI and its sectoral driving factors were examined. Results show that global AEI has declined by about 9% during 2000–2014. Electricity dominated (46%) global AEI composition and it pulled down global AEI with merely 1%, which was primarily brought by the falling share of electricity in global economy rather than electricity AEI decline. Regionally, AEI in most world areas (39/44) has declined, only five economies (Turkey, Brazil, Mexico, Indonesia, and India) held a rising AEI, which was motivated by their increased AEI and consumption proportion for carbon-intensive sectors. The marked rise in domestic energy intensity and import share were critical factors for the increased AEI of electricity, metal, and non-metal sectors in these regions. Policymaking targeted at adopting measures that reduce domestic energy intensity and consumption share for carbon-intensive sectors in economically backward regions was recommended for the purpose of mitigating global AEI.

Towards a cleaner energy system: Estimating the odds of transitioning to an energy-efficient state

At the heart of the global transition to cleaner energy systems is also an inherent mandate of ensuring a transition to an energy-efficient economy. This study applies a two-regime Markov switching dynamic model to examine the possibility of transitioning to an energy-efficient state, using Ghana as the subject of analysis. Since changes in energy intensity do not necessarily suggest improvements in energy efficiency, the study controls for the effect of income, trade, and energy price. The results show that Ghana has a higher chance of transitioning to an energy-efficient state but a relatively lower possibility for such efficiency to persist. What's more, it would take 1.2 y for the said efficiency to persist. The study recommends a broad policy overhaul targeting institutional restructuring, energy financing, and regulatory efficiency as means to facilitate the prospects of transitioning and improving energy efficiency persistence.

Exploring the driving mechanism and the evolution of the low‐carbon economy transition: Lessons from OECD developed countries

AbstractThe low‐carbon transition involves achieving the coordinated development between economic development and carbon reduction. This paper utilises an integrated decomposition and econometric framework to reveal the influence mechanism and convergence evolution of carbon emission intensity (CEI) across 29 OECD countries in the period 1992–2018. The country‐level low‐carbon transition efforts are then investigated. The results imply that: (1) The reduction in CEI is primarily attributed to the changes in economic development and energy intensity. The carbon emission coefficient effect also negatively influences CEI. The impact of energy consumption per capita, however, is negligible. (2) According to econometric analysis, energy intensity, energy mix, carbon emissions per capita and energy use per capita have positive impacts on CEI. Specially, GDP per capita shows an inverted U‐shaped curve relationship with CEI. (3) There is evidence of convergence in CEI across developed countries. Energy intensity plays a crucial role in accelerating the CEI convergence process, implying a half‐life of 3 years. (4) Fifteen European countries have made strong low‐carbon transition efforts among all countries. Denmark has made the largest efforts, South Korea the least. This study provides important implications for low‐carbon economy transition.

Global Emissions of Hydrogen Chloride and Particulate Chloride from Continental Sources.

Gaseous and particulate chlorine species play an important role in modulating tropospheric oxidation capacity, aerosol water uptake, visibility degradation, and human health. The lack of recent global continental chlorine emissions has hindered modeling studies of the role of chlorine in the atmosphere. Here, we develop a comprehensive global emission inventory of gaseous HCl and particulate Cl- (pCl), including 35 sources categorized in six source sectors based on published up-to-date activity data and emission factors. These emissions are gridded at a spatial resolution of 0.1° × 0.1° for the years 1960 to 2014. The estimated emissions of HCl and pCl in 2014 are 2354 (1661-3201) and 2321 (930-3264) Gg Cl a-1, respectively. Emissions of HCl are mostly from open waste burning (38%), open biomass burning (19%), energy (19%), and residential (13%) sectors, and the major sources classified by fuel type are combustion of waste (43%), biomass (32%), and coal (25%). Emissions of pCl are mostly from biofuel (29%) and open biomass burning processes (44%). The sectoral and spatial distributions of HCl and pCl emissions are very heterogeneous along the study period, and the temporal trends are mainly driven by the changes in emission factors, energy intensity, economy, and population.

The impact of energy industry structure adjustment on digital media application technology

The adjustment of the energy industry structure is affected by many factors such as natural conditions, social conditions, economic development speed, industrial structure, and policy and technological development level. This paper studies the factors affecting the effectiveness of the energy industry’s structural adjustment and supply structure and the effectiveness of the supply structure of digital media application technology talents, and clarifies the various factors that affect the effectiveness of the digital media application technology talent structure to achieve low carbonization in this structure. The internal reason of the effect, and the rules for the interconnection and interaction of various elements under certain conditions. In view of the deficiencies of existing research, this paper proposes the narrow and broad impacts of industrial structure adjustment on energy intensity. The former refers to the changes in energy intensity caused by pure changes in industrial structure, and the latter refers to changes in industrial structure through economic operations. The comprehensive impact of various variables on energy intensity. Theoretical analysis shows that the rationalization and advancement of the energy industry structure is conducive to the growth of employment in digital media application technology and the change in the employment structure of digital media application technology. The adjustment of industrial structure has potential and long-term effects on the employment of digital media application technology professionals. Therefore, while optimizing the industrial structure, we should also accelerate the professional transformation of digital media application technology, especially the active research and development of new technologies, including the use of low-emission or even zero-emission energy use technologies, such as the comprehensive utilization technology of solar and wind energy, and high-tech The production technology, environmental protection technology, energy-saving technology, etc. of the technology industry will take a new road to industrialization.

The design and performance of charged particle detector onboard the GECAM mission

The Gravitational wave highly energetic Electromagnetic Counterpart All-sky Monitor (GECAM) is dedicated to detecting gravitational wave gamma-ray bursts. It is capable of all-sky monitoring over and discovering gamma-ray bursts and new radiation phenomena. GECAM consists of two microsatellites, each equipped with 8 charged particle detectors (CPDs) and 25 gamma-ray detectors (GRDs). The CPD is used to measure charged particles in the space environment, monitor energy and flow intensity changes, and identify between gamma-ray bursts and space charged particle events in conjunction with GRD. CPD uses plastic scintillator as the sensitive material for detection, silicon photomultiplier array as the optically readable device, and the inlaid Am-241 radioactive source as the onboard calibration means. In this paper, we will present the working principle, physical design, functional implementation and preliminary performance test results of the CPD. As a result, the energy range of electron, gamma-ray detection efficiency and dead time are tested to be better than the indexes required through the ground calibration experiment.

The drivers of energy intensity changes in Chinese cities: A production-theoretical decomposition analysis

Quantifying the drivers of energy intensity change could provide valuable information for policy analysis and making. This study applied production-theoretical decomposition analysis to investigate the driving forces behind the energy intensity changes in Chinese cities between 2006 and 2017. The empirical results show that technological change and capital-energy substitutions were responsible for 6% and 4% of the fall in energy intensity on average, respectively, while technical efficiency contributed to about 2% of the increase in energy intensity, with significantly different magnitude across various cities. We further classified the sample cities based on the simultaneous effect of decomposition results, identifying the effective combinations of drivers on energy intensity reduction for different types of cities. The classification reveals that double-drivers and triple-drivers of energy intensity change prevail, which explains why energy intensity significantly fell in eastern and central regions but increased in the western region. Furthermore, the cities were categorized into four groups based on the differences in changes in energy intensity and energy efficiency. It reveals the key measures used to reduce energy intensity, and identifies that over a half of the cities with similar changing trends of energy intensity might be misjudged by policymakers with respect to energy efficiency performance.

Inter-fuel substitution and decomposition analysis of energy intensity: Empirical evidence from Iran

Iran economy has high energy intensity and CO2 emission compared to other peer countries. This study adopts the Logarithmic Mean Divisia Index (LMDI-I) method to decompose the total energy intensity changes Δ(E/GDP) of the Iranian economy by considering inter-fuel substitution impacts of the economic sectors over the period of 2004–2017. Results show that the total energy intensity of Iran's economy increased by 43.59 barrels of oil equivalent to IRR 1000 (Iranian Rial at a constant price in 2011). Furthermore, results indicate that while inter-sector structural change, per capita GDP impact, and sectoral energy intensity impact lead to an increase in total energy intensity, household energy intensity impact leads to a decrease in total energy intensity during the research period. Extended findings from decomposition analysis demonstrate that household, services, and agriculture sectors have decreasing impact, while industry, power plant and transportation sectors have increasing impact on Δ(E⁄GDP). In addition, removing the consumption of kerosene in household sector and reducing its share in most economic sectors has caused decline, whereas high consumption of fuel oil, gasoline and gasoil compared to other energy carriers, has led to an increase in total energy intensity. As such, it is concluded that substituting various types of fuels in economic sectors couldn't reduce energy intensity. Besides, serious revisions are needed in the energy policies and energy efficiency programs. It is recommended that non-price policies along with price-based energy policies be implemented to reduce energy intensity and CO2 emission.

Storm‐Scale and Fine‐Scale Boundary Layer Structures of Tropical Cyclones Simulated With the WRF‐LES Framework

AbstractPrevious studies demonstrated that the tropical cyclone (TC) boundary layer (TCBL) contains small‐scale coherent structures, such as roll vortices and tornado‐scale vortices (TSVs), and they play important roles in energy transport and intensity changes in TCs. However, little is known about how horizontal resolutions can affect storm‐scale and fine‐scale structures in the TCBL with the grid spacing decreasing from the turbulent gray zone (100 m–1 km) to the large‐eddy scale (<100 m). In this study, numerical experiments with the large eddy simulation technique are used to investigate the effect of the model horizontal resolution on the simulated TC‐scale and fine‐scale structures in the TCBL. The simulated TC tends to have a shallower BL, a lower BL jet associated with stronger near‐surface vertical wind shear in the TCBL when the grid spacing decreases from 333 to 111 m or 37 m. When the grid spacing decreases to 111 m or 37 m, the fine‐scale coherent structures and near‐surface wind streaks associated with roll vortices and TSVs can be simulated, and the characteristics of the simulated turbulent kinetic energy in the TCBL are in good agreement with observational studies. With the grid spacing of 111 and 37 m, no significant differences in TC intensity are found in terms of instantaneous maximum and azimuthal‐mean maximum wind speeds. This study suggests that simulations with the horizontal grid spacing of 100 m or less can simulate the small coherent structures in the TCBL and their effect on TC intensity.

Study the Trends in Energy Consumption Change for the Transport Service Sectors

This research analyzes the energy consumption of transport service sectors in Vietnam and its changing trend in the past twenty-five years using Input-Output (IO) tables and Logarithmic-mean Divisia index (LMDI) method. IO table of 28 economic sectors in 1996, 2000, 2007, 2012 and 2018 is used to determine energy consumption, in which the transport service sector was always the third or second largest energy consumer, accounting for between 9% and 16% of total energy consumption. LMDI method is used to define influencing factors including transport activity, transport structure, transport intensity, and energy intensity. In these four impacts, the change of transport activity contributes the largest effect (occupied 74.3%), followed by the change of energy intensity (occupied 17.7%) of total increased share for energy consumption. Among the transport service sectors, it is found that Freight transport service by road played the mainstream role in the increasing trends of energy consumption in the period of 2007-2018. In order to improve the energy efficiency of the sector, investments in green transport technologies and modernization of trucks to be more efficient and eco-friendlier will be the key contributors.

What are the main factors that influence China’s energy intensity?—Based on aggregate and firm-level data

Understanding the main factors influencing energy intensity in China is important to reduce total energy consumption and protect the environment. Based on the OECD–WTO Database of Trade in Value-Added, we subdivide the secondary sector and employ the LMDI method to analyze how much changes in industrial structure and intra-industry energy intensity contribute to the overall change in energy intensity. The results prove that the change in intra-industry energy intensity is the main factor that influences the overall change in energy intensity in recent years. Furthermore, we use firm-level energy consumption data that cover various regions, scales and kinds of energy consumption, and employ firm fixed effects regression to analyze intra-industry influencing factors and find that the scale effect exists for coal, oil and electricity efficiency; foreign-invested firms have lower energy intensity; and firms in coal-rich areas have higher coal intensity. Our results show that if firms’ energy intensity decreases to the level of firms with lower energy intensity within the industry, total energy consumption will decrease considerably without changing the output or industry structure. This paper will give implications for developing countries, where it is difficult to make industrial structural changes to reduce energy consumption.

Efficiency improvement, structural change, and energy intensity reduction: Evidence from Chinese agricultural sector

China is now facing the challenge of modernizing its agricultural sector while reducing agricultural energy intensity to slow down the growth of agricultural energy consumption. This paper aims to investigate the determinants of changes in agricultural energy intensity (CAEI) in China. Specifically, this paper first uses the Log-mean Divisia Index method to decompose CAEI into changes in efficiency component (CEC) and changes in structure component (CSC) based on national time-series data during 1981–2017 and then employs panel data models with fixed effects to discern the determinants of CAEI, CEC, and CSC based on provincial data during 1991–2015. The results are as follows: (1) CEC dominates CAEI with a correlation coefficient of 0.9927, while the impact of CSC is limited; (2) an inverted U-shaped relationship exists between CEC and income. Capital and labor significantly improve CEC, while the impact of energy price is significantly negative; (3) a U-shaped relationship is observed between CSC and income. Capital and labor significantly decrease CSC; (4) a U-shaped relationship exists between CAEI and capital. Energy price and income significantly reduce CAEI, while labor has the opposite effect. Finally, it is suggested to improve farmers' environmental awareness, deepen agricultural capital accumulation, and deregulate rural energy prices.