Energy-related Carbon Dioxide Emissions Research Articles

A tale of two generations: Justice in cities for a low-carbon world

According to the greenhouse gas (GHG) protocol for cities, GHGs are responsible for an estimated 75 per cent of global energy-related carbon dioxide emissions. This represents a key opportunity to tackle climate change. With the year 2023 and, in particular, the month of July delivering record-breaking temperatures, this demonstrates a clear need to accelerate decarbonisation and, in this context, to reduce the carbon footprint of cities. Further, acting on urban development addresses a number of key United Nations Sustainable Development Goals (UNSDGs). Many cities, however, are struggling to break away from a reliance on carbon and some are economically dependent on it. This paper seeks to investigate, through an energy justice lens, some first considerations of how to secure just and sustainable urban regeneration. It posits that energy justice, and its five core principles, is a useful analytical tool for considering the justice and development concerns related to the transition to a low-carbon world. The cities examined in brief in this comparative study are Bordeaux in France, Venice in Italy, Ho Chi Minh City in Vietnam and Bangkok in Thailand. The research posits that despite different trajectories, cities can play a leading role in ensuring justice and sustainability in our low-carbon world.

A tale of two generations: Justice in cities for a low-carbon world

According to the greenhouse gas (GHG) protocol for cities, GHGs are responsible for an estimated 75 per cent of global energy-related carbon dioxide emissions. This represents a key opportunity to tackle climate change. With the year 2023 and, in particular, the month of July delivering record-breaking temperatures, this demonstrates a clear need to accelerate decarbonisation and, in this context, to reduce the carbon footprint of cities. Further, acting on urban development addresses a number of key United Nations Sustainable Development Goals (UNSDGs). Many cities, however, are struggling to break away from a reliance on carbon and some are economically dependent on it. This paper seeks to investigate, through an energy justice lens, some first considerations of how to secure just and sustainable urban regeneration. It posits that energy justice, and its five core principles, is a useful analytical tool for considering the justice and development concerns related to the transition to a low-carbon world. The cities examined in brief in this comparative study are Bordeaux in France, Venice in Italy, Ho Chi Minh City in Vietnam and Bangkok in Thailand. The research posits that despite different trajectories, cities can play a leading role in ensuring justice and sustainability in our low-carbon world.

Machine learning approaches for predictions of CO2 emissions in the building sector

The building sector has historically accounted for around 50% of the energy-related carbon dioxide (CO2) emissions on a global scale. As a result, it attracts significant attention as part of the worldwide effort to decarbonize the energy system. This paper presents and compares a variety of Machine Learning (ML)-based approaches for long-term predictions of CO2 emissions from buildings until the year 2050. These approaches include Linear Regression, ARIMA (Autoregressive Integrated Moving Average), Shallow Neural Networks, and Deep Neural Networks, all conducted using both univariate and multivariate modelling and with different approaches for the Features Extraction process; namely, the lagged values approach and the polynomial transformation. The analysis is conducted for different regions of the world including Brazil, India, China, South Africa, the United States, Great Britain, the World-average and the European Union. A variety of tests are conducted to evaluate and compare the predictive performance of the different ML approaches.

Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms

Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set.

Knowledge Structure of New Energy Vehicle Policy Research

Globally, transport is responsible for 23% of energy-related carbon dioxide emissions and 80% of these emissions are attributable to road transport. Significant transformations, including extensive electrification of the sector, are necessary to achieve climate change goals. To understand new energy vehicle (NEV) policy research, we explore the status, knowledge base and research frontiers of NEV policy research by studying 355 papers collected from the Web of Science™ (WoS) Core Collection database. We map NEV policy research trends and knowledge structure development using knowledge domain technology and bibliometric techniques. The knowledge base analysis shows that: (a) NEV policy formation and evaluation; (b) policy incentives and consumer adoption; and (c) consumer preferences towards NEV adoption are all essential knowledge foundations in NEV policy research and development (R&D). The efficiency of NEV policy, cost-effectiveness of alternative fuel vehicles (AFVs), consumer preferences for NEV adoption, hydrogen energy and fuel cell vehicles, climate policy and CO2 emissions are five main lines of research in NEV policy studies. With the highest number of publications from Tsinghua University, China is the most active country in NEV policy research. Energy Policy, Sustainability and Journal of Cleaner Production are the core journals and Energy and Fuels and Environmental Sciences are the core disciplines of NEV policy research. The findings of this analysis help policymakers and researchers to navigate the literature on NEV, provide a clear map of existing works, identify the gaps and recommend promising avenues for future studies.

Low-carbon optimization in commercial buildings through data mining

The government of China commits to achieve peak carbon dioxide emissions by 2030. According to the United Nations Environment Programme, nearly 40% of energy-related carbon dioxide emissions are attributable to the building sector while building operations account for over 70% of carbon emissions. How to quantitatively analyze the factors related to operational carbon emissions under the circumstance of rapid growth of data volume is the key problem to be solved. This article explored the main factor affecting carbon emissions of commercial office buildings based on data mining and analyzed all carbon sources using the variation and deviation method in the city of Beijing, China. It is found that electricity, trains, airplanes, and hotels have a great impact on the building’s operation stage carbon emission, and targeted carbon emission reduction policies and measures can be made to reduce the sample’s carbon emission.

Anthropogenic CO2 emission estimates in the Tokyo metropolitan area from ground-based CO2 column observations

Abstract. Urban areas are responsible for more than 40 % of global energy-related carbon dioxide (CO2) emissions. The Tokyo metropolitan area (TMA), Japan, one of the most populated regions in the world, includes various emission sources, such as thermal power plants, automobile traffic, and residential facilities. In order to infer a top–down emission estimate, we conducted an intensive field campaign in the TMA from February to April 2016 to measure column-averaged dry-air mole fractions of CO2 (XCO2) with three ground-based Fourier transform spectrometers (one IFS 125HR and two EM27/SUN spectrometers). At two urban sites (Saitama and Sodegaura), measured XCO2 values were generally larger than those at a rural site (Tsukuba) by up to 9.5 ppm, and average diurnal variations increased toward evening. To simulate the XCO2 enhancement (ΔXCO2) resulting from emissions at each observation site, we used the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by meteorological fields at a horizontal resolution of ∼1 km from the Weather Research and Forecasting (WRF) model, which was coupled with anthropogenic (large point source and area source) CO2 emissions and biogenic fluxes. Although some of the diurnal variation of ΔXCO2 was not reproduced and plumes from nearby large point sources were not captured, primarily because of a transport modeling error, the WRF–STILT simulations using prior fluxes were generally in good agreement with the observations (mean bias, 0.30 ppm; standard deviation, 1.31 ppm). By combining observations with high-resolution modeling, we developed an urban-scale inversion system in which spatially resolved CO2 emission fluxes at >3 km resolution and a scaling factor of large point source emissions were estimated on a monthly basis by using Bayesian inference. The XCO2 simulation results from the posterior CO2 fluxes were improved (mean bias, −0.03 ppm; standard deviation, 1.21 ppm). The prior and posterior total CO2 emissions in the TMA are 1.026 ± 0.116 and 1.037 ± 0.054 Mt-CO2 d−1 at the 95 % confidence level, respectively. The posterior total CO2 emissions agreed with emission inventories within the posterior uncertainty, demonstrating that the EM27/SUN spectrometer data can constrain urban-scale monthly CO2 emissions.

Energy-related carbon dioxide emission factors from combustion of gaseous methane fuels

Abstract The topic of this paper concerns energy-related carbon dioxide emission factors from the combustion of gaseous methane fuels. It is a very current subject, as methane fuels, mainly of fossil origin, are one of the primary energy carriers used in industrial and municipal applications for energy purposes. Consequently, the emitted carbon dioxide contributes to the intensification of the greenhouse effect in the atmosphere. The problem of the quantities characterising emissions from the combustion of methane fuels is complicated by the diversity of these fuels. This paper presents background information on the gaseous hydrocarbon fuels and the composition of the main types of natural gas. The structure of natural gas consumption in Poland, depending on the sectors of application for particular types of natural gas, is presented. On the basis of the existing data, aggregated energy-related carbon dioxide emission factors were estimated and analysed. The analysis was carried out for high-methane natural gas (HM), colliery gas (CG) and nitrified natural gas (N)—for the years 1988–2021. Trends in fuel consumption and energy-related carbon dioxide emission factors were determined for the years 1988–2021.

Development and performance comparison of optimized machine learning-based regression models for predicting energy-related carbon dioxide emissions.

Accurate prediction of CO2 emissions for the countries has become a crucial task in decision-making processes for planning energy conversion and usage, supporting the design of effective emissions reduction strategies, and helping to achieve the goal of a sustainable and low-carbon future. Therefore, this study aims to develop a general model that can predict the national CO2 emissions of each country using data from 68 countries with high prediction accuracy based on machine learning regression models. Nine prediction models were developed using Support Vector Regression, Ensemble of Trees, and Gaussian Process Regression algorithms as machine learning methods, and their prediction performances were compared. Additionally, the hyperparameters of these three machine-learning methods were tuned by Bayesian optimization to improve their prediction performance. The test results of the optimized Gaussian Process Regression model (MSE = 106.68, RMSE = 10.328, MAE = 4.904, MAPE = 3.38%, R2 = 0.9998) showed that it was the best prediction model among the all developed models. Additionally, the optimized Gaussian Process Regression model gave very robust results in predicting CO2 emissions in many countries, indicating that it can be used reliably and with high accuracy as a promising prediction model.

Towards Multiphase Modeling and Simulation of Alkaline Water Electrolysis through Pore-Resolved Foam Electrodes

In today’s world, concern is growing about the future of energy. Despite very ambitious international climate goals by 2050, global energy-related carbon dioxide (CO2) emissions keep increasing. In order to tackle this problem, hydrogen (H2) might become a significant part of the solution since it is a way to produce, store, move and use energy in a clean way. However, more than 95% of the actual hydrogen production is made of grey hydrogen, e.g. H2 produced from fossil fuels, which leads to high CO2 emissions [1]. One way to decarbonise this energy vector is to use renewable energies (solar panels, wind turbines, etc.) to produce green hydrogen via water electrolysis.Among the various technologies available to perform water electrolysis, alkaline water electrolysis (AWE) is the most mature one. In general, AWE consists of two planar electrodes separated by a certain distance and operating in a liquid alkaline electrolyte solution (e.g. KOH, potassium hydroxide). Recent studies show that it is possible to significantly increase the performance of such electrolysers by substituting the traditional gap-cell design by a zero-gap cell [2,3]. To do so 3D electrodes such as foams and 3D-printed structures are used, leading to an increase of the surface area and as a result, to a higher hydrogen production rate. These 3D electrodes are incorporated between the planar current distributors at either side of a membrane or diaphragm, reducing the interelectrode distance to the thickness of the separator. In the case of traditional cells, the hydrogen bubbles will tend to evolve only from the surface of the 2D current distributor plates, while for zero-gap cells gases evolve inside the porous structure. The process can be improved even more by forcing the electrolyte flow, favouring bubble removal. Finally, a bi-layer configuration with a catalyst layer presenting high specific surface area near the separator along with a porous transport layer (PTL) allows for a very high hydrogen production in the catalyst and an easy evacuation of the bubbles through the PTL.Recent experimental results in our group validate the enhanced performance of a bi-layer foam configuration where a fine foam acts as the catalyst layer and a coarser one as the PTL. In order to have a better understanding of the electrolyte flow behaviour inside these 3D electrodes, Computational Fluid Dynamics (CFD) was used as a tool to simulate the single-phase flow numerically. The purpose of this is to determine optimal flow parameters in order to favour hydrogen bubble removal while benefiting from the high surface area of the 3D electrodes. Up to now, it appears that this kind of analysis has rarely been addressed in the literature [4].In our specific case, the foams used as the catalyst and the PTL have characteristic pore sizes of 450 µm and 3000 µm, respectively. In order to obtain an accurate geometric representation, the foams were scanned using high-resolution micro-CT (computed tomography). A computational mesh was then obtained by reconstructing the highly detailed scanned data. The incompressible Navier-Stokes equations for the single-phase flow were solved using the Finite Element Method (FEM) implemented in MigFlow software [5]. In Figure 1 we present results from a simulation with the PTL width being three times the width of the catalyst layer, and the inlet velocity 1 m/s. It appears that the electrolyte velocity in the direction perpendicular to the flow is the highest in the area close to the interface between the catalyst and the PTL, meaning that this configuration would help extracting hydrogen bubbles off the catalyst layer, towards the coarser porous region.It is important to note that the results obtained in this case were not considering the gas phase. We are currently working on the development of a multiphase pore-resolved model to see whether the assumption of optimal bubble removal is confirmed for multiphase flow. To this end, a Eulerian-Eulerian two-fluid model must be implemented for the liquid electrolyte and the gaseous bubbles. One key aspect is to correctly define closure relations for the interfacial exchange terms that appear in the momentum equations. Hydrogen bubble formation, growth and escape from the porous electrodes might also be considered. This work is a first step towards the modeling of the whole alkaline water electrolysis process through 3D porous electrodes. Figure 1

Investigating the Direct and Spillover Effects of Urbanization on Energy-Related Carbon Dioxide Emissions in China Using Nighttime Light Data

Cities are the main emission sources of the CO2 produced by energy use around the globe and have a great impact on the variation of climate. Although the implications of urbanization and socioeconomic elements for carbon emission have been extensively explored, previous studies have mostly focused on developed cities, and there is a lack of research into naturally related elements due to the limited data. At present, remote sensing data provide favorable conditions for the study of large-scale and long-time series. Also, the spillover mechanism of urbanization effects on the discharge of carbon has not been fully studied. Therefore, it is necessary to distinguish the types of influence that various urbanization factors have on emissions of CO2. Firstly, this study quantifies the urban CO2 emissions in China by utilizing nighttime lighting images. Then, the spatio-temporal variations and spatial dependence modes of CO2 emissions are explored for 284 cities in China from 2000–2018. Finally, the study further ascertains that multi-dimensional urbanization, socio-economic and climate variables affect the discharge of carbon using spatial regression models. The results indicate that CO2 emissions have a remarkable positive spatial autocorrelation. Urbanization significantly increases CO2 emissions, of which the land urbanization contribution towards CO2 emissions is the most important in terms of spillover effects. Specifically, the data on urbanization’s direct effects reveal that CO2 emissions will increase 0.066%when the urbanization level of a city rises 1%, while the spillover effect indicates that an 0.492% emissions increase is associated with a 1% rise of bordering cities’ average urbanization level. As for the socio-economic factors, population density suppresses CO2 emissions, while technological levels boost CO2 emissions. The natural control factors effect a remarkable impact on CO2 emissions by adjusting energy consumption. This study can provide evidence for regional joint prevention in urban energy conservation, emission reduction, and climate change mitigation.

Decoupling Analysis of Energy-Related Carbon Dioxide Emissions from Economic Growth in Poland

This paper addresses the conditions behind energy-related carbon dioxide (CO2) emissions in Poland following its accession to the European Union. The study analyzed some energy, fossil fuel, economic, and environmental indicators, such as energy use, CO2 emissions, driving factors, decoupling elasticity status, and decoupling effort status. It relied on the Kaya identity and Logarithmic Mean Divisia Index (LMDI) in determining the drivers of CO2 emissions. As shown by the results, between 2004 and 2020, energy consumption in Poland grew at an average annual rate of 0.8%, while fossil fuel carbon emissions declined at 0.7% per year. Energy intensity was found to be the key force behind the reduction in CO2 emissions, whereas rapid economic growth was the main driver of CO2 emissions. While other factors, i.e., carbon intensity, energy mix, and population, also contributed to reducing the emissions, they had a much smaller—if not marginal—effect. In turn, the decoupling elasticity analysis showed that Poland mostly witnessed strong decoupling states, which means a strong decoupling of CO2 emissions from economic growth. Furthermore, it follows from the decoupling effort analysis that strong decoupling effort statuses prevailed throughout the study period, suggesting that the changes in the considered factors significantly contributed to reducing CO2 emissions. However, both the decoupling elasticity indices and the decoupling effort indices revealed the presence of an adverse trend. The above means that Poland witnessed a decline both in the strength of decoupling CO2 emissions from economic growth and in the efficiency of policies and measures designed to reduce CO2 emissions.

Optimal design of inter-state hydrogen fuel cell vehicle fueling station with on-site hydrogen production

With about 28% of all energy-related carbon dioxide emissions in 2018, the transportation sector is one of the major contributors to carbon dioxide production. Hydrogen fuel appears to be a promising alternative to fossil fuels in the direction of green transportation. As a result, one of the most important aspects of achieving this technology is developing an appropriate infrastructure to fuel this energy source. In this research, we plan to study and evaluate a hydrogen-releasing system (HRS) located in the middle of the road. This system is powered by the clean energy of the solar panel, generates hydrogen locally with the help of an electrolyzer, and contains both low-pressure and high-pressure reservoirs. TRNSYS software was used to model this system. Therefore, utilizing the trial-and-error method, the dimensions and operating parameters of each component of this system have been determined depending on the state of the energy supply from the panel and refueling in the three cascade reservoirs. Based on the modeled panels from existing catalogs, the findings demonstrate that the modeled system with 28 series panels and 100 parallel panels would be able to supply electricity to the entire system while maintaining a stable pressure in the high-pressure reservoirs. The percentage of hydrogen emitted to the environment based on this system was nearly equal to 15.5%, which was an appropriate amount for the system.

An Evaluation of the Energy-Related Carbon Dioxide Emissions From China's Light Sector to Achieve Sustainable Development Goals.

As a high-energy-consuming sector, China's light sector should have received more attention for its carbon emissions (CO2e). However, the literature on energy-related CO2e in China's light sector is limited at present. This paper aims to assess the impact of China's light sector on CO2e. This paper applies the energy consumption technique, input-output analysis technique, and structural decomposition model to analyze China's light sector energy-related CO2e and emission reduction from the input-output perspective. The results show that the energy structure effect, energy intensity effect, and input structure effect are the main restraining factors for the growth of the light sector energy-related CO2e, which are caused by the expansion of the energy utilization structure on the supply side of the light sector. The final demand effect is the factor promoting the growth of the light sector energy-related CO2e. It reveals that the final demand products in the light sector still have high environmental degradation features. Policymakers should actively enhance and rationally adjust the demand for the light sector in numerous industries to avoid the resource waste caused by the excessive expansion of the light sector.

A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector

The building sector has traditionally accounted for about 40% of global energy-related carbon dioxide (CO2) emissions, as compared to other end-use sectors. Due to this fact, as part of the global effort towards decarbonization, significant resources have been placed on the development of technologies, such as active buildings, in an attempt to achieve reductions in the respective CO2 emissions. Given the uncertainty around the future level of the corresponding CO2 emissions, this work presents an approach based on machine learning to generate forecasts until the year 2050. Several algorithms, such as linear regression, ARIMA, and shallow and deep neural networks, can be used with this approach. In this context, forecasts are produced for different regions across the world, including Brazil, India, China, South Africa, the United States, Great Britain, the world average, and the European Union. Finally, an extensive sensitivity analysis on hyperparameter values as well as the application of a wide variety of metrics are used for evaluating the algorithmic performance.

Single Score Environmental Performances of Roof Coverings

Buildings and constructions are responsible for a great amount of global energy and energy-related carbon dioxide emissions. Because of these negative impacts, there is an increase in Life cycle assessment research in the construction sector to measure these effects and evaluate the sustainability performances. Life cycle assessment is a tool that can facilitate the decision-making process in the construction sector for material selection, or for the selection of the best environmentally friendly option in the building component level or building level. In this study, a comparative life cycle assessment analysis is conducted among 12 roof coverings of 1 square meter in the 60-year lifetime of a building. Impact categories that are available in environmental product declarations and included in this study are the global warming potential, ozone depletion potential, acidification potential, eutrophication potential, photochemical ozone creation potential, abiotic depletion potential of non-fossils and abiotic depletion potential of fossils resources. To facilitate the decision-making process, panel and monetary weightings are applied to convert environmental product declaration data of seven impact categories into one single-score. Monetary weightings applied in the study are in Euro 2019, differentiating itself from other comparative life cycle assessment studies. The single-score results are ranked and compared. R04 has the best performance for all panel weightings, while for monetary weightings, R03, R07 and R08 have the best performance for EPS, MMG and EVR, respectively. As a result, for 12 roof coverings, the weighted results could not address one single roof-covering material for numerous reasons. Among the weighting methods, panel weighting sets show more similarity in ranking results, while monetary-weighting sets results are more diverse.

Energy Consumption Analysis and Characterization of Aerospace Manufacturing Facilities in the United States–A Step towards Sustainable Development

In this study, information on energy usage in the United States (U.S.) aerospace manufacturing sector has been analyzed and then represented as energy intensities (kWh/m2) to establish benchmark data and to compare facilities of varying sizes. First, public sources were identified and the data from these previously published sources were aggregated to determine the energy usage of aerospace manufacturing facilities within the U.S. From this dataset, a sample of 28 buildings were selected and the energy intensity for each building was estimated from the data. Next, as a part of this study the energy data for three additional aerospace manufacturing facilities in the U.S. were collected firsthand. That data was analyzed and the energy intensity (kWh/m2) for each facility was calculated and then compared with the energy intensities of the 28 buildings from the sample. Three different indicators of energy consumption in aerospace manufacturing facilities were used as comparators to assist facility managers with determining potential energy savings and help in the decision-making process. On average, aerospace manufacturing facilities in the United States spent 4 cents for each dollar of sale on energy. The energy intensity (kWh/m2) and the power intensity (W/m2) for each facility were calculated based on the actual facility energy bills. The power intensity for these facilities ranges from 34 to 134 W/m2. The energy intensity ranged from 232 to 949 kWh/m2. We found that the power intensity could be used to estimate energy consumption when the annual operating hours of the facility are considered. and to estimate the energy-related carbon dioxide emissions.

Economic-Comparative Study for Carbon Neutrality During Ships Docking and in Port Operations: A Path Towards Maritime Sector Decarbonization

According to the International Energy Agency (IEA) reports, the maritime sector was responsible for around 2% of global energy-related Carbon Dioxide (CO2eq) emissions in 2020 (i.e., 714 Mton). Currently, Heavy Fuel Oil (HFO) is used for supplying main and auxiliary Internal Combustion Engines (ICEs) on board ships, therefore it is responsible for local and global pollution. As matter of fact, HFO is contaminated with several different compounds including aromatics, sulfur, and nitrogen, making emission upon combustion more polluting compared to other fuel oils. In order to reduce pollution in maritime sector, the International Convention for the Prevention of Pollution from Ships (MARPOL) has prohibited ships using of HFO in ports. In this purpose, new solutions involving cleaner fuels as well as new propulsion technologies are needed.This paper is aimed to evaluate the environmental advantages that can be reached by replacing the HFO with low emission fuels and alternative power technologies. In particular, three cases have been investigated: i) ICEs fed by diesel, ii) Fuel Cell Systems fed by “Yellow” hydrogen, iii) Fuel Cell Systems fed by “Green” hydrogen. The analysis has been carried out by applying the Well-to-Waves (WTW) method for estimating and comparing the avoided CO2eq and NOx emissions. Moreover, the economic feasibility has been carried out by estimating the investment and operating costs.

A revaluation of carbon intensity factors through the carbon decomposition approach in a developing economy

The issue of climate change and its impact on every field of life has increased manifold during the 4.0 industrial revolution. We explore the driving factors of a sector-level carbon intensity which is essential to determine the targeted emissions reduction strategy in the developing economy. To execute this purpose, the study has been integrated by joining production and index decomposition with a spatial-temporal decomposition analysis to estimate the comparative performance of a sector. We cover nine significant factors for this purpose: the economic efficiency effect, the intensity effect, the gross domestic product gap effect, the structure effect, and the energy use efficiency effect. Moreover, this study utilized an updated set of data from three economic sectors, including the agriculture, services, and industrial sectors during 2006–2019 to estimate the energy-related carbon dioxide emission. According to our results based on the above classification, the performances of all these sectors are relatively either above, average, or below level. The economic and energy usage efficiency effects have a high association with one another, and both have above-average performance; however, the GDP gap effect has a lower performance. The service sector shows mixed results, whereas the performance of the agriculture sector remained unsatisfactory in this perspective.

Energy use and energy-related carbon dioxide emissions drivers in Egypt's economy: Focus on the agricultural sector with a structural decomposition analysis

Investigating drivers of energy use and its associated CO2 emissions changes is important for sustainable development. Using input-output structural decomposition analysis, this paper decomposed the energy use and resulting CO2 emissions drivers of changes in Egypt's wide economy, particularly in its agricultural sector, between 1972 and 2014. Fuel sources dominating energy use and energy-related CO2 emissions in the agriculture sector were explicitly identified. Affluence and demographic effects were the dominant drivers at both investigation levels. Household and government final consumption changes and exports expansion were important determinants behind changes in energy use and resulting CO2 emissions at the aggregate level from the final demand destination perspective. In the agricultural sector, petroleum and oil were the dominant fuel types. The technological effect was the main driver restraining energy use growth at the aggregate level. However, technological improvements have not been sufficient to make up for the evolving economy and consumption levels. Conversely, technological changes adversely impacted energy-related CO2 emissions at the economy-wide level, and both considered environmental indicators in the agricultural sector. Overall, upgrading technology and improving the final demand structure will be critical for Egypt to promote decoupling in the future.