Energy Consumption Carbon Emissions Research Articles

Carbon emission of energy consumption of the electric vehicle development scenario.

To explore the impact of promotion of electric vehicles on carbon emissions in China, this paper used the principal component analysis (PCA)-logistic regression model to predict the demand for traditional vehicles, and used the scenario analysis method to analyze the proportion of electric vehicles in traditional vehicles qualitatively. Then this paper calculated the carbon emissions during the power generation process based on the existing power structure. In addition, the IPCC carbon emission calculation method is used to compare the CO2 emissions produced by electric vehicles and fuel vehicles with similar vehicle quality while driving the same distance and consuming different energy sources. The results showed that the CO2 emissions of electric vehicles accounted for only 37.05% of fuel vehicles. By 2050, the annual electricity demand of electric vehicles will be 828.7, 776.9, and 752.1 billion kWh under the radical scenario, the reference scenario, and the negative scenario, respectively. According to the current power structure, the carbon emissions will be 1.2, 1.1, and 1 billion tons, respectively. The rapid growth of electric vehicles has a substantial impact on the grid load. Studying the changes in CO2 emissions from energy substitution is significant to formulate the development strategy of the automobile industry and adjust energy structure policies.

Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

Construction and Application Analysis of Carbon Emission Influence Factor Model of Energy Consumption in Mining Industry

With the proposal of China’s “double carbon goal,” as a high energy‐consuming industry, it is urgent for the mining industry to adopt a low‐carbon development strategy. Therefore, in order to better provide reasonable suggestions and references for the low‐carbon development of mining industry, referring to the methods and parameters of the 2006 IPCC National Greenhouse Gas Inventory Guidelines and China’s Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial), a carbon emission estimation model is established to estimate the carbon emission of energy consumption of China′s mining industry from 2000 to 2020. Then, using the extended Kaya identity, the influencing factors of carbon emission in mining industry are decomposed into energy carbon emission intensity, energy structure, energy intensity, industrial structure, and output value. On this basis, an LMDI model is constructed to analyze the impact of five factors on carbon emission from mining industry. The research shows that the carbon emission and carbon emission intensity of energy consumption in China’s mining industry first rise and then fall and then rise slightly. The carbon emission intensity in recent three years is about 2 tons/10000 yuan. The increase in output value is the main factor to increase carbon emission. The reduction in energy intensity is the initiative of carbon emission reduction. The current energy structure of mining industry is not conducive to carbon emission reduction.

Analysis on the Change Characteristics of the Correlation between Land Use Structure and Energy Consumption and Carbon Emissions in Kunming from 1997 to 2017

This study takes Kunming City, Yunnan Province, China as the research area, to provide reference basis for revealing the change law of land use structure and energy consumption and carbon emissions in Kunming, optimizing land use structure and realizing the development of low-carbon city. Based on the data of land use structure and energy consumption in Kunming from 1997 to 2017, based on the estimation of total energy consumption carbon emissions, carbon intensity and per capita carbon emissions, the correlation between land use structure and energy consumption carbon emissions in Kunming has been calculated and analyzed in the past 20 years. Results: 1) The total amount of carbon emissions in Kunming has increased significantly in the past 20 years. It increased from 34.46 × 105 t to 95.09 × 105 t, an increase of about 2.8 times. 2) The types of land use with the highest correlation between land use structure and total carbon emissions of energy consumption, carbon emission intensity and per capita carbon emissions are urban and village and industrial and mining land (0.8258), cultivated land (0.8733) and garden land (0.7971) respectively. 3) The correlation between construction land and total carbon emissions is greater than that of agricultural land. Conclusion: There is a close correlation between land use structure and carbon emissions from energy consumption in Kunming.

Holistic sustainability assessment of hybrid Al–GnP-enriched nanofluids and textured tool in machining of Ti–6Al–4V alloy

There is a need to replace non-sustainable conventional coolant (emulsion) with eco benign high-performance cutting fluids such as biodegradable nanofluids that exhibit sufficient lubrication and cooling properties. Also, the sustainability performance of titanium-based alloys can be improved with the geometric alteration on the tool rake zone and by the proper cooling-lubrication mechanism. In the present study, the holistic sustainability of external turning of titanium (Ti–6Al–4V) alloy under two different cutting environments (Al–GnP nanoparticle–based minimum quantity lubrication (MQL) with textured carbide tools and conventional emulsion) is investigated. Novel empirical models for cycle time, machining power, embodied and electrical energy consumption carbon emission, and production cost were developed. For both environments, machining experiments were performed until the cutting tool reaches its life. All sustainable indicators were measured and compared with the conventional process. Results showed that textured carbide cutting tools in the presence of Al–GnP nanoparticle–based MQL-assisted machining (hybrid process) are sustainable environmentally and economically. At the very-high (130 m/min) cutting speed, the sustainable hybrid machining process consumed 1.50% less specific cumulative energy (S_CEC), emitted 5.96% less specific CO2, and incurred 4.33% less specific production cost (S_PC) compared with flood-assisted machining. Finally, it is concluded that the presence of hybrid Al–GnP nanofluids in line texture has the potential to act as lubricant/coolant in turning processes.

Modelling energy demand response using long short-term memory neural networks

We propose a method for detecting and forecasting events of high energy demand, which are managed at the national level in demand side response programmes, such as the UK Triads. The methodology consists of two stages: load forecasting with long short-term memory neural network and dynamic filtering of the potential highest electricity demand peaks by using the exponential moving average. The methodology is validated on real data of a UK building management system case study. We demonstrate successful forecasts of Triad events with RRMSE ≈ 2.2% and MAPE ≈ 1.6% and general applicability of the methodology for demand side response programme management, with reduction of energy consumption and indirect carbon emissions.

Impact of marginal and intergenerational effects on carbon emissions from household energy consumption in China

As an important part of urban carbon emissions, the carbon emissions from household energy consumption are increasing with the continuous improvement of Chinese urbanization. This study divides family population into 4 groups: adolescents (aged 0–17), young people (aged 18–44), middle-aged people (aged 45–59), and elderly people (aged 60 and older) and develops a calculation model for carbon emissions of household energy consumption in accordance with the latest standards of the World Health Organization based on Chinese Family Panel Studies in 2016. Results showed that the marginal and intergenerational effects coexist in various types of household energy consumption: Families with 3–5 people have the most economical consumption scale and the largest marginal sharing effect; multigenerational families or families with a large proportion of middle-aged and older people are beneficial in reducing household energy consumption. This paper suggests it is important to fully consider the impact of the intergenerational effect, marginal effect, and cultural shock caused by the electronic information age on household energy consumption when formulating standards of household energy consumption.

Performance assessment of rice production based on yield, economic output, energy consumption, and carbon emissions in Southwest China during 2004–2016

Global warming, energy consumption (EC), and food security have led to increasing concern about carbon emissions (CEs) from agriculture, especially CEs from rice cultivation in China. However, there are few studies on CEs from rice production in Southwest China (SWC) (including Yunnan Province (YNP), Sichuan Province, Guizhou Province, and Chongqing Municipality (CQM)), which has complex topography and climatic conditions. Here, a set of indicators based on yield, economic output, EC, and CEs were proposed to describe the performance of rice production in SWC during 2004–2016. The research results reflected that (1) the share of indirect EC of rice production was 75.46% in SWC, more than 90% of which originated from nitrogen fertilizer, compound fertilizer, and pesticides; (2) direct CEs contributed to 55.20% of the total CEs in SWC during the study period, and fertilizer production and utilization were the largest contributor (45.03%) to CEs in this area, followed by paddy fields (38.61%); (3) CQM had the worst performance in SWC in terms of CE intensity of economic output and profit from land use; (4) YNP had the greatest EC intensity based on yield, the greatest CE intensity based on sowing area and yield, and the worst production structure and comprehensive performance; and (5) the structure of rice production was optimized to different degrees in SWC, but no obvious improvement was seen in relationships between yield, economic output, EC, and CEs in this area. Therefore, measures should be taken to reduce the EC intensity and CE intensity of rice production in YNP, and rice plantation methods with high yield and fine quality should be emphasized in the other three administrative regions.

Empirical Analysis of Environmental Constraints and Influencing Factors in Beijing-Tianjin-Hebei Region

This paper focuses on carbon emissions problem and adopts the “bottom-up” method proposed by IPCC to calculate the energy consumption carbon emission of leading industries in the Beijing-Tianjin-Hebei region. And constructed the Tapio decoupling index model to analyze the “decoupling relationship” between the development of leading industries and energy consumption and carbon emissions in various regions, so as to quantify the environmental constraint levels of different industrial development. Finally, this paper uses LMDI model to decompose the carbon emission factors, and explores the environmental impact of industrial leading industries in Beijing-Tianjin-Hebei region from the four dimensions of carbon emission intensity, energy intensity, economic development and population size, in order to effectively promote the optimization of industrial structure in Beijing-Tianjin-Hebei region and to achieve pareto optimization.

Theoretical Assessment of Sustainability Principles for Renewable Smart Air-ConditioningTheoretical Assessment of Sustainability Principles for Renewable Smart Air-Conditioning

Upon an improvement in the quality of life, air-conditioning has generally been applied. Nevertheless, environmental and health issues related with the use of air-conditioning occurs more often. Therefore, this paper aims to theoretically assess the principles of sustainability to achieve sustainability for renewable smart air-conditioning. Not only with consideration to the geometry (i.e. system mechanisms and components), fuzzy logic control and proportional-integral-derivative that such studies drawn particular attention to, but with concerns to a matter which has been previously ignored. That is with consideration to the potential which the renewable-based options, advanced smart control technique and profitability measures of air-conditioning reinforces the three pillars of sustainability, and their sustainable indicators as context-specific transformations have, to not only eradicate indoor health effects, lower the levels of energy consumption and rate of carbon emissions, but to uncover the significance of and particular contribution renewables and smart control opportunities makes to the sustainability of the system. In meeting this aim and demonstrating the sustainability of the theoretical framework, this paper reveals renewable and smart control system as the fundamental key components of the air-conditioning as it promotes to reduce levels of energy consumption and lower carbon emissions, vis-à-vis establish a comfortable and healthy indoor environment as an exercise in the sustainable theoretical framework whose status as renewable smart air-conditioning not only tackle poor indoor air quality but also combat global warming and climate change.

Analysis of factors affecting carbon emissions in Tianjin

According to the IPCC carbon emission calculation method, Tianjin's carbon emissions were calculated from 2000 to 2017, and the main factors affecting carbon emissions were analyzed and studied using the LMDI model system. The results show that Tianjin’s energy consumption carbon emissions continued to increase from 2000 to 2017, and have declined significantly in recent years. Among the carbon emissions of energy consumption, coal produced the most carbon emissions, followed by crude oil and coke; Among the factors that affect Tianjin’s carbon emissions from 1997 to 2017, economic development has the largest positive effect, and energy consumption intensity has the largest negative effect; Tianjin’s energy consumption is unreasonable, and it mainly depends on coal, crude oil and other energy sources.

Research on the factors affecting carbon emission of energy consumption in the Beijing-Tianjin-Hebei region

Quantitative analysis of the factors affecting carbon emissions has important guiding significance for reducing regional carbon emissions in the region. Therefore, this paper first systematically analyzes the changes in the total carbon emissions of energy consumption in the Beijing-Tianjin-Hebei region. Then, the improved STIRPAT model is used to analyze the influencing factors of carbon emissions from energy consumption. Finally, the co-integration test and error correction model are used to study the long-term equilibrium relationship and short-term changes of carbon emission and its influencing factors (economic level, population size, technological level and energy structure) in the Beijing-Tianjin-Hebei region.

Research on Decoupling of China’s Logistics Demand and Energy Consumption Carbon Emission

At present, China pays more and more attention to the development of green logistics, while the logistics industry is still in the stage of extensive development, deviating from the green, low-carbon and efficient development concept. Therefore, the analysis of the characteristics of energy consumption and carbon emissions in the logistics industry has important theoretical and practical significance for the formulation of effective energy-saving and emission reduction strategies. Using the carbon emission coefficient method to calculate the logistics industry carbon emissions, and using the decoupling theory to analyze the correlation between the logistics freight volume and the logistics industry energy consumption and carbon emissions.The results show that China’s logistics industry is trying to change the development mode, adapt to the impact of policy and economic environment.So as to adapt to the needs of the logistics market, logistics industry gradually transform to the complex industry.

Analysis on the variation rule and influencing factors of energy consumption carbon emission intensity in China's urbanization construction

Abstract On the basis of literature review and current situation analysis, models were established to carryout scientific measurement for carbon emissions and its intensity in China in this paper. With full consideration to the actual situation of energy consumption growth and carbon emission intensity controlling of energy consumption in new-type urbanization construction, the Kaya model was revised. With population intensity of GDP production, urbanization rate, employment rate of urban population, per capita energy consumption of urban employed population and carbon emission density of energy consumption selected as independent variables, test models of multivariate linear correlation were established for new-type urbanization construction, energy consumption growth and carbon emission intensity controlling of energy consumption, and the empirical equilibrium equation and error correction equation were determined through stability test, lag order test and co-integration test. The results are shown as follows: the urban employment rate (UER) has the greatest impact on carbon emission intensity, the per capita urban employment energy consumption (PEC) has the least impact on carbon emission intensity, and the degree of other factors in proper order is: urbanization rate (UR), population intensity of GDP production (PG) and carbon emission density (CD).

Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

Empirical Study on the Decomposition of Carbon Emission Factors in Agricultural Energy Consumption

The development of low-carbon agriculture is an effective way to maintain sustainable development of agriculture. By using Kaya’s identities to study the factors affecting China’s agricultural energy consumption carbon emissions, it is found that technical factors, low-carbon agricultural factors, rural living standards, and indirect urbanization. Factors and population size factors are important factors influencing the carbon emissions of agricultural energy consumption. Based on this, the paper uses LMDI index analysis method to decompose the above factors and the contribution rate. It is found that both technical factors and low-carbon technology factors can reduce agricultural carbon emissions, and the emission reduction ability of agricultural low-carbon technology factors is stronger than that. Agricultural technology factors. The total population change can drive carbon emissions positively, but the driving force is not strong. Urbanization indicators are driving a weaker positive for agricultural carbon emissions. To this end, the paper proposes that in the process of developing modern agriculture, it is necessary to strengthen the research and development of low-carbon agriculture technology, and promote the development of urbanization in an orderly manner in order to achieve the goal of low-carbon agriculture and sustainable development.

Household carbon emission characteristics and adaptive strategies for low carbon community planning

Urban communities are important sector for energy saving and emission reduction. Characterization of household carbon emission is critical to the planning of low-carbon communities. Taking data from five communities in Nanjing, China as the empirical research object, this study explores the relationship between household consumer activities and direct carbon emission of household energy consumption based on the Consumer Lifestyle Approach. Using regression analysis, we identify the characteristics of the emission and the four most significant impact factors. Adaptive low carbon strategies are further suggested as how to influence consumer activities by planning of urban communities.

Research on optimal collaborative method for microgrid environmental and economic dispatch in grid-connected mode

In recent years, real-time optimisation of microgrid dispatch with minimal total costs of energy generation and emission has been an urgent problem. In this paper, a collaborative method was considered for microgrid connected mode. At first, we constructed the microgrid schematic diagram of energy flow. Then, a mathematical model was presented for dispatch optimisation environmentally and economically. After defining the objective function according to the energy consumption, maintenance and systemic carbon emissions, two types of constraints were given including the single input-output balance and the overall energy flow balance. Thirdly, we analysed the cooperative co-evolutionary genetic algorithm and applied it to a large number of cases. The better optimisation performance and convergence rate were obtained by comparing the method with the traditional genetic algorithms. The experimental results show it may play a significant role in the optimal scheduling of distributed energy generations in grid-connected mode.

Carbon emissions, energy use, real GDP per capita and trade matrix in the Indian economy-an ARDL approach

The study analyzes the effects of carbon dioxide emissions, energy use, gross fixed capital formation, real GDP per capita, exports and imports on Indian economic growth. The study seeks an answer to the following question: When examining the relationships that exist between these parameters, what are the driving forces behind the growth trajectory of the Indian economy? An ARDL model was applied to the analysis of annual data so as to determine co-integration on variables and the nature of the relationship for the period 1971–2014. Empirically, it was found that in the short-term, Real Gross Domestic Product per Capita (LGDPP) for the Indian economy is affected by its past value, Gross Fixed Capital Formation (LGCAPP), Energy Consumption (LENERP) Carbon Emissions (LCO2P) and Imports (LIMPP); however, in the long-term, Gross Fixed Capital Formation (LGCAPP) and Exports (LEXPOP) played a significant role. The error correction model revealed the existence of some co-integration relationship for the entirety of the variables in the study for the short-term and long-term. It is concluded that policies should have clear alternative renewable energy consumption targets in order to achieve energy efficiency and achieve sustainable growth of the economy, while reducing carbon dioxide emissions.

Decomposition Analysis of Carbon Emissions from Energy Consumption in Beijing-Tianjin-Hebei, China: A Weighted-Combination Model Based on Logarithmic Mean Divisia Index and Shapley Value

The Beijing-Tianjin-Hebei (B-T-H) region, who captures the national strategic highland in China, has drawn a great deal of attention due to the fog and haze condition and other environmental problems. Further, the high carbon emissions generated by energy consumption has restricted its further coordinated development seriously. In order to accurately analyze the potential influencing factors that contribute to the growth of energy consumption carbon emissions in the B-T-H region, this paper uses the carbon emission coefficient method to measure the carbon emissions of energy consumption in the B-T-H region, using a weighted combination based on Logarithmic Mean Divisia Index (LMDI) and Shapley Value (SV). The effects affecting carbon emissions during 2001–2013 caused from five aspects, including energy consumption structure, energy consumption intensity, industrial structure, economic development and population size, are quantitatively analyzed. The results indicated that: (1) The carbon emissions had shown a sustained growth trend in the B-T-H region on the whole, while the growth rates varied in the three areas. In detail, Hebei Province got the first place in carbon emissions growth, followed by Tianjin and Beijing; (2) economic development was the main driving force for the carbon emissions growth of energy consumption in B-T-H region. Energy consumption structure, population size and industrial structure promoted carbon emissions growth as well, but their effects weakened in turn and were less obvious than that of economic development; (3) energy consumption intensity had played a significant inhibitory role on the carbon emissions growth; (4) it was of great significance to ease the carbon emission-reduction pressure of the B-T-H region from the four aspects of upgrading industrial structure adjustment, making technological progress, optimizing the energy structure and building long-term carbon-emission-reduction mechanisms, so as to promote the coordinated low-carbon development.