Energy Intensity Research Articles

Heterogeneous technology-induced global CO2 emission reduction and emission forecasting since the Kyoto era

While identifying the drivers of global CO2 emission is crucial for climate change mitigation, the heterogeneous technology-related drivers (e.g., technological change and efficiency of energy and CO2 emission) were ignored to a large extent at a global scale, hindering to formulate heterogenous climate policies. Moreover, the projection of CO2 emission was also not well compared for countries. Here, the study investigated the heterogeneous technology-related drivers of CO2 emissions in time and space simultaneously in 42 major emitter countries over 1998–2020 by extending the spatiotemporal production-theoretical decomposition models, and compared the different performances for forecasting CO2 emission by traditional time-series models and several machine learning models. Key findings as follows: first, drivers of CO2 emissions exhibit significant heterogeneity across countries where the effects of energy usage technology gap and CO2 emission technology gap were negative drivers for USA, South Korea, and the Czech Republic and potential energy intensity effect was the negative driver in countries like China, Russia, Japan, and India. Second, the effects of within-GDP per capita and within- population size were the important drivers affecting global CO2 emission difference. Third, general regression neural network achieved the best forecasting performance on average compared with other models in the study. The study highlights the importance of formulating climate policies based on heterogeneous technology and emission forecast modeling.

The research of the impact of energy efficiency on mitigating greenhouse gas emissions at the national level

Improving energy efficiency is considered to be important and cost-effective strategies to reduce greenhouse gas emissions, which is crucial in the face of global climate change. This study hypothesizes that there are significant differences in the impact of energy efficiency on economic and energy systems. Using quantile regression method, the impact of economic and energy characteristics on energy-related greenhouse gas emissions in 120 countries over the period 2010–2020 is investigated. Detailed analyses are conducted by dividing the countries into 12 groups based on gross national income per capita (Atlas method) and the share of resource rents in gross domestic product. A macro-level system of indicators for assessing energy efficiency is proposed, which can be linked to the economic and energy characteristics of the systems: National Energy Efficiency, Energy Intensity Ratio, Primary Energy Production Efficiency, Industrial Sector Energy Efficiency, Share of Low Carbon Energy, Population Energy Efficiency. The findings show that (1) global energy efficiency has remained largely unchanged over the decade; (2) there are differences in energy efficiency between fossil fuel producing and non-fossil fuel producing countries; (3) the factors that have an inverse effect on emissions in fossil fuels dependent countries include industrial energy efficiency and population energy efficiency, which are far from exhausting their potential; (4) despite the recognized importance of energy efficiency, its impact on reducing greenhouse gas emissions globally has been unnoticeable. The study highlights that Sustainable Development Goals 7 and 13 on energy affordability and emission reductions are conflicting and require harmonization of mechanisms to achieve them, such as balancing climate change mitigation investments, identifying the potential for energy efficiency improvements across economy or improving energy saving practices. The overall conclusion suggests that there is an urgent need to review current decarbonization and energy efficiency initiatives, given the lack of significant global progress in reducing greenhouse gas emissions or improving energy efficiency between 2010 and 2020.

Correlation between energy consumption and economic growth – based on the perspective of panel vector autoregressive analysis

ABSTRACT Efforts are underway to balance economic development and ecological preservation by transforming the energy structure. This study uses the Cobb-Douglas production function to examine the relationship between energy and economic growth. Through panel data and regression models, 32 countries are analyzed in terms of global energy consumption and economic development. Countries are further grouped based on energy intensity and economic level to explore dynamic relationships. Previous studies lacked understanding of this relationship under varying conditions, while this study proposes a novel approach. Results indicate a one-way relationship between renewable energy consumption and economic growth. Further analysis reveals that countries with higher energy intensity and economic level experience a negative impact on economic growth from increased renewable energy consumption. These research findings have implications for government decision-making.

Spatial distribution of energy consumption: Integrating climate and macro-statistics for insights from clustering and sensitivity analysis

Limited comprehensive methods exist for studying spatial energy consumption distribution, integrating statistical and energy data. This paper introduces a novel approach for analyzing residential heating and cooling energy demand distribution. It employs clustering algorithms to study climate variables’ impact on energy demand distribution and assesses building energy demand intensity regionally, taking China as a case study. Initially compiling a dataset comprising climate characteristics, socioeconomic factors, and energy demand through data collection and simulation, the study compares clustering algorithms, highlighting the effectiveness of K-means in clustering high-dimensional climate-energy datasets. K-means analysis reveals temperature-based daily methods significantly affect building energy intensity, alongside factors like radiation intensity and humidity impacting regional energy demand variably. Additionally, climate’s influence on residential building energy consumption intensity varies regionally, with total energy demand influenced by population and economic factors. This paper offers insights for energy management and policy formulation.

Network energy use not directly proportional to data volume: The power model approach for more reliable network energy consumption calculations

AbstractIt is commonly assumed that data volume and network energy consumption are directly proportional, a notion perpetuated by numerous studies and media coverage. This paper challenges this assumption, offering a comprehensive examination of network operations to explain why the relationship between energy consumption and data volume is nonlinear. The power model approach is explored as an alternative methodology for calculating network energy consumption providing a more reliable representation of network energy use. The power model demonstrates that simple energy intensity calculations, expressed as kilowatt hours per gigabyte of data, are insufficient for accurately estimating real‐world network energy consumption.

Assessing the potential for energy efficiency improvements in Latin America and Caribbean

This study assesses energy intensity trends in the Latin America and Caribbean region, identifies key regional drivers for energy efficiency improvements, and proposes policy recommendations using a rich panel dataset of 25 Latin American countries over the last 20 years. The results of the Fisher Ideal Index Decomposition, followed by the regression analysis, demonstrate that sectoral energy efficiency improvements account for most of the historical energy intensity declines in the region, also highlighting the importance of energy prices and regulatory policies. Counterfactual simulations show potential for significant improvements in otherwise stagnant energy intensity in the region by eliminating fossil fuel subsidies and achieving better national planning, strengthening implementation and governance capacity, and improving access to energy efficiency financing mechanisms.

Decoupling state of urban development and carbon emissions and its driving factors and predictions: A case study of Chengdu metropolitan area

Prioritizing the reduction of carbon emissions in key developing urban centers is a significant step toward achieving China's carbon peaking and neutrality goals. We examined the Chengdu metropolitan area as a case study. We proposed a coupled method for inversely estimating nighttime light and carbon emissions from energy consumption, enhancing the accuracy of fitting at the county scale and addressing the issue of missing energy consumption data. We integrated the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) model for comprehensive analysis, mitigating the limitations arising from the use of a singular model. Furthermore, we employed a multi-scenario analysis with an extended STIRPAT model to forecast the carbon emission trajectory of the Chengdu urban agglomeration and elucidate potential pathways for achieving carbon peaking and carbon neutrality goals. The findings indicated the following:(1)carbon emissions are predominantly concentrated in regions characterized by high levels of urban development. Forested land serves as the primary carbon sink; however, its capacity in the Chengdu metropolitan area fails to offset carbon emissions from energy consumption;(2)factor decomposition analysis revealed that economic development was the principal driver of carbon emissions, leading to an increase of 23.7964 million tons. Conversely, energy intensity emerged as the primary mitigation factor, reducing carbon emissions by 11.0677 million tons;(3)The Chengdu metropolitan area is projected to reach a carbon peak by 2030, with the multi-factor coordinated optimization (MFO) model representing the most favorable trajectory, with a projected peak of approximately 15.6998 million tons. However, to achieve carbon neutrality by 2060, there is still a surplus of approximately 10.4808 million tons.

Sustainable Development of European Countries: The Climate Component

This article provides a cause-and-effect analysis of the impact of carbon emissions on climate change and on the sustainable development of European countries. Despite the consolidated efforts of the world community, the global amount of greenhouse gas emissions is not decreasing, leading to irreversible climate change. The objective of this paper is to explore Europe’s progress towards climate neutrality in the context of ensuring sustainable development and achieving the goals of the European Green Deal. The article aims to establish an econometric relationship between the amount of carbon emissions and the energy intensity of GDP based on statistical data from European countries, and using the example of Ukraine during a full-scale invasion to demonstrate factors that influence greenhouse-gas- emissions growth despite GDP. A significant aspect of this study is an analysis of data from 2012–2022, which indicates that Europe has reduced CO2 emissions and is successfully moving towards climate neutrality. Key efforts of EU countries in preventing climate change and transitioning to renewable energy sources are reflected in the context of the Green Deal. Based on econometric calculations, the direct relationship between the amount of carbon emissions and energy intensity of GDP in European countries was confirmed with a probability of 0.95. The obtained interdependence allows one to predict the amount of CO2 emissions based on known values of energy intensity of GDP during stable economic development. Special attention was paid to Ukraine and the increase in CO2 emissions due to the war. The authors concluded that the war disrupted the relationship between GDP and CO2 emissions, leading to a 23% increase in emissions in 2022. Overcoming the climate crisis and ensuring sustainable development requires a global decarbonisation strategy. However, effective climate policy is impossible without achieving peace.

Study on the influence of green finance development on industrial energy consumption

ABSTRACT During high-quality economic development, reducing energy intensity is a key step towards reducing environmental pressures. Considering that the industrial sector is the main energy-consuming sector, green finance can complement traditional environmental regulation while allocating financial resources. Therefore, this research paper aims to investigate the connection between the development of green finance and the intensity of industrial energy consumption. To accomplish this, a dynamic spatial Durbin model is constructed using panel data from 30 regions in China spanning the period from 2006 to 2019. Research revealed that: China’s industrial energy consumption intensity has a significant time lag and spatial lag. Industrial energy intensity is influenced by the spatial distribution of green finance, which in turn has a negative effect on the growth of industrial energy intensity. This means that as green finance develops in the surrounding area, it also decreases the energy consumption intensity within local industries.

Energy intensity convergence among Chinese provinces: a Theil index decomposition analysis

China, the world’s largest carbon emitter, has one of the most stringent provincial emissions reduction programs, incorporated into its Five-Year National Plan to reduce carbon emissions. However, the widening energy intensity gap between provinces poses a great challenge for carbon reduction. In this study, we analyze the convergence of Energy intensity (EIC), i.e., the time-dependent decrease of differences among regional energy intensity over time focusing on a data set of 30 Chinese provinces from 2000 to 2015. Our goal is to identify the provinces that are responsible for the observed divergence in energy intensity and identify the factors causing that divergence in each individual case. The Theil index is used to capture inter-provincial energy inequality. We use the LMDI decomposition analysis to identify the drivers of energy inequality (energy consumption structure, energy efficiency, and industrial structure). The results suggest that reducing energy intensity in Inner Mongolia, Xinjiang, and Hebei is the key to solving China's increasing energy intensity “gap” dilemma. The factors causing the energy intensity divergence in Inner Mongolia and Xinjiang are related to lagging economic growth and low energy efficiency, which impedes carbon emission reductions significantly. The factors causing the divergence of energy intensity in Hebei are rooted in its heavy industrial structure. Our findings are directly applicable to crafting regional energy policy with more targeted and practical emission reduction programs.

Do new-type urbanisation pilots narrow urban household carbon emission differences between cities? A temporal-spatial within-between decomposition analysis

The relationship between urbanisation and carbon emissions has been widely discussed. However, whether new-type urbanisation, which aims to achieve green development, helps reduce urban household carbon emissions (UHC), remains unclear. By expanding the temporal-spatial within-between refined Laspeyres index decomposition model with data from 236 Chinese cities from 2015 to 2021, this study investigated the differences in UHC between pilot and non-pilot cities under the new-type urbanisation pilot policy and further explored the drivers of these differences. The temporal decomposition results highlighted the changes and their drivers in UHC between pilot and non-pilot cities. UHC have increased in both types of cities, with the increase linked to higher urbanisation rates, greater carbon intensity, and rising incomes. However, the energy intensity and the urban-rural income gap effects helped curb the extent of the increase. Although a higher level of UHC existed in the pilot cities, the new-type urbanisation pilot policy helped narrow the gap in UHC between pilot and non-pilot cities, thus curbing UHC. The spatial decomposition results further explored the causes of the UHC gap between pilot and non-pilot cities. The within-between decomposition results suggested that, for pilot cities, those with higher UHC than the pilot-group average should close the gap with the group average, whereas those with lower UHC than the national average should close the gap with the national average. The findings present a useful reference for balancing urbanisation with a low-carbon household sector.

SPECIFIC ENERGY CAPACITY OF PROCESSING AND ENERGY EFFICIENCY FOR PROCESS OF GRINDING WITH WHEELS FROM SUPERHARD MATERIALS

The analysis of modern research shows that when evaluating the specific energy intensity of the abrasive processing process, one should pay attention not only to the indicators of grinding power and material removal rate, but also to the indicator of the wear of the abrasive tool, which we will show below for the grinding tool made of superhard materials. It is shown that the traditional method of estimating the specific energy capacity based on the ratio of the grinding power to the processing productivity does not provide an adequate solution, since with it the specific heat capacity of processing exceeds the specific heat capacity of melting of the processing material by almost an order of magnitude. Therefore, it is the application of a new approach to the assessment of the specific energy intensity of diamond grinding, taking into account the wear of the working layer of the diamond wheel, and makes it possible to estimate the indicators of the specific energy intensity of grinding and the energy efficiency coefficient. It has been proven that when estimating the specific energy capacity of grinding metal-ceramic composite materials consisting of a low-melting and refractory component, the latent heat capacity of melting of the low-melting component should be taken as the basis. It is shown that the plastic mode of grinding occurs precisely when the specific energy capacity of grinding, taking into account the wear of the wheel, becomes close to the specific heat capacity of melting of a brittle material.

Decomposition Analysis of CO2 Emissions in Greece from 1996 to 2020

To meet Greece’s commitments under the Kyoto Protocol (KP) and the UN Framework Convention on Climate Change (UNFCCC), it’s crucial to dissect the primary influencers of its greenhouse gas emissions. This study utilized the logarithmic mean Divisia index method in its additive form to delve into the main drivers behind carbon dioxide emissions in Greece from 1996 to 2020. The analysis revealed that the primary catalyst for the rise in CO2 emissions was the growth in GDP. Conversely, advancements in energy efficiency, as indicated by a decrease in energy intensity, played a significant role in reducing CO2 emissions. While population growth contributed to an increase in emissions, a decline in emissions intensity showcased a reduction in emissions per unit of output or consumption. The period from 1996 to 2020 witnessed a 38% change in emissions, demonstrating the effectiveness of Greece’s environmental policies. These findings highlight the pressing need for a sustained focus on enhancing renewable energy sources (RES) penetration and ensuring comprehensive attention across all sectors to further reduce emissions.

Lead-acid battolysers for hydrogen cooking: A comparison with electric cooking for sub-Saharan Africa

A battolyser combines the function of battery and electrolyser in one device, i.e. it provides both electrical energy storage and a means to produce hydrogen. A battolyser with lead-acid chemistry has recently been proposed, and this has potential as a particularly low-cost solution. Here, the battolyser is considered for the production of hydrogen as a cooking fuel (“hCooking”) in sub-Saharan Africa, a region where cooking typically employs polluting fuels (firewood and charcoal). The more conventional approach for decarbonisation of cooking is the introduction of electric cookers (e.g. hotplate, induction hob, pressure cooker) which can be powered by PV and possibly battery storage; accordingly these electric cooking (“eCooking”) systems are considered as the competing decarbonised technology. Multi-objective optimisation is used to design both battolyser and eCooking systems for a notional off-grid community, with solar PV as the main energy source. Objectives are the minimisation of net present cost and lifetime greenhouse gas emissions, and Pareto frontiers are produced to show the play-off between these. Results show that a battolyser system could eliminate 95.6 % of CO2 emissions when compared with a baseline using charcoal, at an annualised cost of $507 per household, over a system lifetime of 20 years. However, eCooking systems appear superior to the battolyser, with the cleanest battery + eCook system achieving 95.8 % emissions reduction at annualised cost $422/household. More generally, hCooking systems are nearly always Pareto dominated by eCooking systems, even under a realistic range of sensitivity scenarios. This result is due to the inherently higher energy intensity of cooking over a flame compared to the eCooking options. Priorities to make the battolyser a more viable solution include extending its lifetime as far as possible, cheaper PV systems, and improved hydrogen burner efficiencies. We also show that eCooking together with some continued use of charcoal may be the cheapest possible cooking solution, whilst simultaneously curtailing 60 % of lifetime greenhouse gas emissions.

The Dynamics of Energy-Related Carbon Emissions and Their Influencing Factors in the Yangtze River Delta, China

Chinese cities are pursuing an energy transition to decouple energy-related carbon emissions (ERCEs) from economic growth. Despite numerous studies focusing on the factors influencing carbon emissions, few have quantitatively analyzed their respective contribution rates, thus leaving a gap in effectively guiding policies. This study took 16 cities in the Yangtze River Delta (YRD) as the study area. The decoupling between ERCEs and economic growth was analyzed during 2000–2020, and the contribution rates of different factors were explored. The results showed that the total ERCEs increased from 413.40 million to 1265.86 million tons during 2000–2020, increasing by over three times. Coal and oil were the dominant energy sources in most cities, but natural gas consumption increased from 0.15% to 5.96%. Moreover, 14 cities showed a decoupling status, indicating a certain win–win situation between economic growth and ERCE reduction. Economic growth greatly increased ERCEs, with its contribution rate ranging from 114.65% to 493.27% during 2000–2020. On the contrary, energy structure and energy intensity both contributed to reducing ERCEs in most cities, and their maximum contribution rates reached −32.29% and −449.13%, respectively, which were the main forces for the win–win situation. Finally, carbon reduction proposals are put forward, which provide theoretical support for achieving the “Double Carbon” goal in the YRD.

Study on saline-alkali water distillation system by reflection enhanced solar heating

A saline-alkali water distillation system by reflection enhanced solar heating is proposed to treat the saline-alkali soil washing water. In this system, the heat concentration collector employs heat collecting plate to receive solar radiation energy, and converges to small-area steam generating tube through efficiently heat transfer. Without expensive optical tracking equipment, the system also employs an external reflector to reflect the solar radiation to the surface of the heat concentration collector. Thereby the energy flow density is further increased, and the useful energy and vaporization intensity is promoted. The latent heat of the steam is reused by heat recovery to enhance the energy utilization efficiency. The optimal inclination angles of reflector in different seasons and system performance were theoretically calculated. In the spring equinox, summer solstice, autumn equinox, and winter solstice, the optimal inclination angles of the reflector were 30°, 45°, 30°, and 15°, respectively. The freshwater production, useful energy, and heat recovery efficiency of the system were investigated by experiments. The daily freshwater production of the system was 17.04 kg, and the maximum production rate was 3.44 kg/h. The reflector increased the useful energy and freshwater production of the system by 33.68% and 35%, respectively. With heat recovery, the initial temperature of the saline-alkali water was increased to 53.8 °C and the freshwater production was increased by 13.16%.

Is the renewable energy intensity convergent in OECD countries? Insights from novel unit root tests with factors and structural breaks

This study extends the research on the convergence analysis of energy-related variables to cover renewable energy intensity by focusing on 26 OECD countries from 1990 to 2021. While there have been several studies on the convergence of different energy variables including renewable energy, the intensity dimension has been largely ignored in the literature. As some non-zero carbon emissions are associated with deploying various forms of renewable energy resources, ignoring their intensity of GDP fails to address the question of efficiency. To address this gap, this study employed a battery of econometric techniques including the novel panel unit root tests that capture abrupt or smooth breaks in the presence of cross-sectional dependence to test for the presence of unit roots in relative renewable energy intensity. The study also examined different clusters of the sample to check the existence of convergence or otherwise at different levels. The results suggest that 21 countries are convergent and only Germany, Japan, Netherlands, Poland and Switzerland are not convergent when the full sample is considered. However, these countries are convergent when other clusters of the countries are examined. Policy implications of the study have been discussed in the paper.

Regional policy options for carbon peaking in the Yangtze River Delta under uncertainty

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity.

Energy Intensity Forecasting Models for Manufacturing Industries of “Catching Up” Economies: Lithuanian Case

The objective of this research was to construct energy intensity forecasting models for key manufacturing industries, with a particular focus on “catching up” European economies. Future energy intensity values serve as the foundation for energy demand forecasts, which are essential inputs for the analysis of countries’ decarbonisation scenarios. The Lithuanian case is analysed in the context of its efforts to reach the economic development level of the most advanced European Union (EU) countries. The scientific literature and energy policy analysis, interdependence (correlation and regression), tendency and case analysis, logical economic reasoning, and graphical representation methods have been applied. The energy intensity forecasts until 2050 were based on historical statistical data of value added and final energy consumption of EU countries from 2000 to 2021. The analysis of historical trends revealed a remarkable decrease in industrial energy intensity in most EU countries, including Lithuania. Given the rapid pace of decline in historical energy intensity, the values observed in individual Lithuanian industries have already reached levels comparable to the most economically advanced EU countries. Four econometric trendlines were employed to construct forecasting models for energy intensity. The results for Lithuania demonstrated that the selected trendlines exhibited a high degree of fit with historical energy intensity data from the EU, as evidenced by their R2 values. Furthermore, the forecasts were shown to be highly accurate, with their MAPEs remaining below 10% in most cases. Nevertheless, the logarithmic trendline was found to be the most accurate for forecasting energy intensity in total manufacturing (MAPE = 4.0%), non-metallic minerals (MAPE = 3.5%), and food, beverages, and tobacco (MAPE = 4.1%) industries, with the exponential trendline in the chemical industry (MAPE = 8.7%) and the moving average in the total manufacturing industry (MAPE = 4.0%), food industries (MAPE = 4.0%), and remaining aggregate industries (MAPE = 14.5%). It is forecasted that energy intensity could decline by 8 to 16% to 1.10–1.20 kWh/EUR in Lithuania’s manufacturing industries by 2050.

The ‘complex’ transition: Energy intensity and CO2 emissions amidst technological and structural shifts. Evidence from OECD countries

The purpose of the paper is twofold. Firstly, we investigate whether technological and structural shifts result in reduced energy intensity and lower CO2 emissions. Secondly, we explore whether such technological and industrial transformations interact and have a joint impact on the environment. We use a PVAR model in first differences for a panel of 34 OECD countries spanning from 1994 to 2019, as well as for a cluster of countries exhibiting the most compelling trend in energy intensity. The findings substantiate our research hypotheses. Notably, technological change exhibits a full impact on carbon dioxide emissions exclusively within the identified cluster. Structural change does not directly impact emissions reduction, yet it appears to act as a catalyst for green innovation and reduced energy intensity. Improvements in energy intensity lead to lower emissions and spur both technological and structural transformations. The technological change driven by R&D activities supports the trend towards services. Such a structural evolution serves as a stimulus for environmentally related innovations. In this context, environmental taxes wield a substantial influence on the environment and the energy transition.