Analysis of Carbon Emissions from Petroleum Energy Consumption in Heilongjiang Province

The need to reduce carbon emission in Heilongjiang Province of China is urgent challenge facing sustainable development. This paper aims to make explicit the problem-solving of carbon emission to find low carbon emission ways. According to domestic and foreign literatures on estimating and calculating carbon emissions and by integrating calculation methods of carbon emissions, it was not possible to consider all of the many contributions to carbon emissions. Calculation model of carbon emissions suitable to this paper is selected. The carbon emissions of energy consumption in mining industry are estimated and calculated from 2005 to 2012, and the characteristics of carbon emission are analyzed at the provincial level. It makes the point that carbon emissions of energy consumption in mining industry can be reduced when we attempt to alter energy consumption structure, adjust industrial structure and improve energy utilization efficiency.

Economic development leads to the widespread use of energy, which results in carbon emissions. In order to determine the correlation between different urban carbon emissions and fiscal growth, a coupling and coordination model of urban carbon emissions and economic growth based on discrete Fourier transform (DFT) is constructed. According to the coupling and coordinated development characteristics of economic growth and carbon emissions, the evaluation index system of the regional economy and carbon emissions is constructed. This paper examines the proportion of primary energy consumption and the influencing factors of carbon emission in a city and constructs the economic growth computational intelligence model and urban carbon emission model. The coupling degree among economic growth, carbon emission, and energy consumption is studied; the coupling standard between economic growth and energy consumption carbon emission is determined; and the carbon emission factor under the method of DFT is introduced. The coupling coordination model between economic growth and carbon emission is constructed, and the interaction mechanism between carbon emission, economic development, and environmental protection is determined. The experimental findings demonstrate that when energy consumption intensity and carbon emission are relatively low, the model’s change in the trend of coupling coordination index is comparable with the real/actual condition and the model is more reliable.

Hunan province energy consumption carbon emissions based on the industrial structure was analyzed with carbon emissions factor method in 2000-2012. Results show that Hunan province’s carbon emissions have a rapid growth in 2000-2012. Since 2007 the growth of carbon intensity is slowly, and there is an emergence of signs of decline. Recently the correlation between the growth of GDP and carbon emissions in Hunan Province becomes weakening, but carbon intensity is still higher. Industry occupies a dominant position in the energy consumption carbon emissions. Since 2007 the proportion of industrial carbon emissions is decreased form 79.41% to 72.30% in 2012, there is an obvious decline. Recently, the growth rate of industrial carbon emissions is relative lower. The growth of carbon emissions from the construction industry and the tertiary industry is the most obvious. Relevant policies should be formulated as soon as possible, to promote the level of construction technology, control energy consumption and carbon emissions per unit of output.

With the establishment of the action plan for the goal of “carbon peaking and carbon neutrality”, how to achieve high-quality agricultural development, help implement the construction of the green Longjiang River, reduce agricultural carbon emissions, and increase the level of agricultural carbon sink is a key problem that must be solved for Heilongjiang Province to achieve the goal of “double carbon”. This article uses the Life Cycle Assessment (LCA) method to estimate the carbon emissions of animal husbandry in Heilongjiang Province and 13 cities from 2000 to 2020. By constructing the Tapio decoupling model, Kaya identity, and the LMDI model, the relationship between animal husbandry economy and carbon emissions in Heilongjiang Province and the driving factors affecting animal husbandry carbon emissions are explored. The results indicate that: (1) From 2000 to 2020, the carbon emissions of animal husbandry in Heilongjiang Province showed an overall slightly upward trend. From the perspective of various emission links, the highest carbon emissions are from the gastrointestinal fermentation environment (42.49%), with beef cattle, cows, and live pigs being the main livestock and poultry in Heilongjiang Province with carbon emissions. (2) The Tapio decoupling model results indicated that from 2000 to 2020, the relationship between the economic development of animal husbandry in Heilongjiang Province and carbon emissions was mainly characterized by weak decoupling. (3) The main driving force behind the continuous increase in carbon emissions from animal husbandry in Heilongjiang Province is the changing factors of agricultural population returns and changes in the production structure of animal husbandry; The driving factors that suppress the increase in carbon emissions from animal husbandry in Heilongjiang Province are changes in animal husbandry production efficiency, population and urban development levels, and population mobility factors. Finally, based on the decoupling effect status and driving factors of decomposition between Heilongjiang Province and its various cities, it is recommended to implement countermeasures and suggestions for the transformation of animal husbandry in the province towards green and low carbon at the macro level. This can be achieved through the adoption of sustainable and eco-friendly practices such as the use of renewable energy sources and the reduction of greenhouse gas emissions. Additionally, promoting research and development in sustainable agriculture and animal husbandry can also contribute to the transformation towards a more environmentally friendly industry.

Achieving carbon peak before 2030 and carbon neutrality in 2060 is the solemn commitment of the Chinese government to the international community. It is important to scientifically identify the key influencing factors and accurately predict carbon peak value and time for achieving the dual carbon targets in China. On the basis of the economic and energy consumption data from 1980 to 2022， a STIRPAT extended multivariate-nonlinear model was built， which was fitted by a ridge regression to examine the relationships between carbon emissions of energy consumption and seven influencing factors， including population， GDP per capita， energy intensity， secondary industry proportion， fossil energy proportion， electrification rate， and urbanization rate. Based on the proposed STIRPAT extended model， predictions of carbon emissions of energy consumption were made for the period from 2023-2035 under three different scenarios. The results showed that： ① There were five factors that increased the carbon emissions including population， urbanization rate， secondary industry proportion， GDP per capita， and electrification rate. The degree of influence decreased in turn. Fossil energy proportion and energy intensity were the two factors that restrained the carbon emissions. The influencing degree of fossil energy proportion was the biggest， and that of energy intensity was the smallest. ② During different stages of economic development， the roles and contributions of the seven factors changed significantly. In particular， the effects of energy intensity， secondary industry proportion， and fossil energy proportion resulted in the turning changes， which reflected the periodical characteristics of carbon emissions in different stages. ③ Carbon emissions of energy consumption will achieve a peak during 2028-2032 in China. The peak was 11.66-12.75 billion tons. Under the baseline scenario， the peak was 12.04 billion tons， which will be fulfilled in 2030. The peak of the low-carbon scenario was 11.66 billion tons in 2028， which was 3.16% lower than that of the baseline scenario. The peak of the high-carbon scenario was 12.75 billion tons in 2032， which was 5.90% higher than that of the baseline scenario. Based on the research results， reasonable suggestions such as accelerating renewable energy development， increasing the electrification rate， optimizing the economic structure， and improving energy efficiency are put forward to ensure that China will achieve its carbon peak target before 2030.

The emission of greenhouse gases poses enormous pressure on current carbon emissions and carbon reduction. Accurate quantification of carbon emissions from coal-fired power plants is of great significance for achieving the dual carbon goal. To enable enterprises to better understand their carbon emissions, this study constructs a carbon emission model and carbon emission data accounting model for coal-fired power plants. Case data calculations and a carbon emission reduction analysis were conducted. The experiment showcases that the carbon sensitivity of the inner side of the boiler under control conditions is higher than that of the operating parameters controlled on the inner side of the steam turbine, with a maximum total value of 16.67 g/MJ; the annual average low calorific value of coal remains between 16,000 kJ/kg; the activity level of coal remains between 30,000 TJ; and the oxidation probability of coal char during combustion fluctuates, with a maximum of 99.8%. In the calculation of coal-fired carbon emissions, the fitting difference between the emissions of generator unit 1 and generator unit 2 is maintained within 2%. Overall, the CO2 emissions of power plants involved in the study are generally high. The model built through this study has well analyzed the carbon emissions of power plants. It is of great significance for the actual carbon emission reduction of coal-fired power plants.

Clarifying the key sectors and influencing factors of carbon emissions from energy consumption is an important prerequisite for achieving the “carbon peaking and carbon neutrality” goals. This study calculated the carbon emissions of fuel combustion in 7 major departments and regional electricity trading of Sichuan Province from 2000 to 2021, and empirically analyzed the impact of energy structure effect, energy intensity effect, industrial structure effect, economic development level effect, and population size effect on the carbon emissions of energy consumption based on the LMDI model. The main research conclusions are as follows: (1) LMDI model has the advantages of no residual and high interpretation. By refining the multi-departments of energy consumption and different types of fuel, it is helpful to improve the accuracy of empirical analysis results. (2) The carbon emissions of energy consumption mainly come from the fuel combustion process. Specifically, the industry sector composed of steel, building materials, chemicals and machinery is the key emission sector, and transportation and residential life are also vital. Regional electricity trading can indirectly reduce the intensity of carbon emissions while ensuring the safety of energy supply. (3) From 2000 to 2021, the energy intensity effect and the economic development level effect were key factors in slowing down and promoting the carbon emission growth of energy consumption in Sichuan Province, respectively. The population scale effect mainly played a positive role in driving carbon emissions, but the impact is small and almost negligible. Before 2012, the energy structure effect and the industrial structure effect were mainly positive driving effects, and after 2012, they all turned into negative inhibitory effects. This was mainly due to the low-carbon transformation of energy structure and the optimization of industrial structure.

Climate change is one of the major challenges currently facing the world. The factors influencing the carbon emission of energy consumption and the future trend are important guidance for proposing scientific carbon reduction strategies to mitigate climate change. In this paper, the Logarithmic Mean Divisia Index (LMDI) model and stochastic impacts by regression population, affluence and technology (STIRPAT) model are established to analyze and predict the carbon emission of energy consumption. The LMDI model is used to factorize the CO2 changes generated by residential domestic energy consumption, and to decompose and analyze the carbon emission factors of residential domestic energy consumption in terms of energy carbon emission intensity, energy consumption structure, energy consumption intensity, economic development, and population to determine the driving factors leading to carbon emission changes; based on the above study, we set up nine different development scenarios and applied the scalable stochastic environmental impact assessment model to project energy carbon emissions in 2035; based on carbon emission prediction and analysis, the CO2 emissions of total energy consumption, total electricity consumption, industrial energy consumption and terminal energy consumption were selected, and the correlation coefficients with relevant climate indicators such as temperature change and humidity change were analyzed, and the stress model of energy consumption on climate change was constructed. The results show that: the correlation coefficients of energy consumption indicators and temperature change indicators all pass the significance test at P = 0.01 level, among which the correlation coefficients with temperature difference are the highest, all of them are greater than 0.9 and pass the significance test at P = 0.001 level; among the indicators of energy consumption, the correlation coefficient between total industrial energy consumption and temperature difference was slightly higher than that of total energy consumption and electricity consumption; the stress relationship between the increase of energy consumption and the temperature difference is consistent with the growth of the third polynomial curve.

The key to coping with climate change is to control carbon emissions from energy consumption. Scientific prediction of energy consumption carbon emissions based on influencing factors is of great significance to the determination of carbon control aim and emission reduction strategies. Given the lack of previous studies on county-level carbon emissions, this paper proposed a systematic approach to study the influencing factors of county-level energy consumption carbon emissions and to predict future emissions. Firstly, the annual energy consumption carbon emissions were calculated based on the method proposed by the Intergovernmental Panel on Climate Change (IPCC). Then the expanded Kaya equation and existing research were combined to select influencing factors for the establishment of the optimal Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, which was used to quantitatively analyze the influencing factors of carbon emissions from energy consumption at the county level. Finally, the emission reduction aims and low-carbon strategies were determined based on scenario analysis. The method was applied to Changxing, a typical county with large energy consumption and carbon emissions. Based on 16 years of data, the STIRPAT carbon emission prediction model was established and the forecast results of future emissions under three different scenarios were obtained. The results indicated that population size, industrial structure, and affluence degree were the three most influential factors, and the influence degree of each factor was quantified to support targeted low-carbon strategies for county-level cities.

Climate change has become a common concern of the international community and a common challenge facing the world. As the world's largest carbon emitter and the second largest economy, China has been committed to protecting the environment and actively implementing low-carbon emission reduction measures. In September 2020, at the 75th United Nations General Assembly, China promised to increase its national independent contribution, adopt more powerful policies and measures, strive to reach the peak of carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060. With the proposal of the "Double Carbon" goal, all walks of life in China are facing great pressure on emission reduction. As a basic industry with high energy consumption, mining industry actively implements the green and low-carbon mining mode, which is the inevitable choice for its sustainable development. Due to the great differences in production among the mining industry segments, this book only studies the carbon emissions of energy consumption. Firstly, it analyzes the carbon emission accounting methods at home and abroad, constructs the carbon emission estimation model of energy consumption in mining industry under the guidance of relevant documents, and calculates the carbon emission of energy consumption in China's mining industry from 2000 to 2020. Secondly, it analyzes the industry development, energy consumption and carbon emission of the mining industry. Then, using the decoupling theory, this research analyzes the decoupling state between the carbon emission of the mining industry and the growth of the industry, and analyzes the reasons for the decoupling. Finally, the impact effect model of carbon emission is constructed by using LMDI theory, and the effects of five main influencing factors and their impact on carbon emission are calculated and analyzed. It is hoped that the research of this book will contribute to the formulation of low-carbon emission reduction measures in the mining industry, and also provide a basis for the in-depth research of mining industry by relevant experts and scholars. Chapters 2-7 of this book are mainly written by Lili Wei, and some contents of chapters 1 and 3 are written by Xiwen Feng. This study is the research result of Shandong Social Science Planning Project ( No.: 17CJJ09). In the process of writing, we refer to the research results of many experts, scholars and research institutions at home and abroad. Thank you whether listed in the references or not! In view of the limited level of researchers, there are inevitable mistakes in the book. Readers are welcome to provide valuable opinions. The email is weilili@sdjzu.edu.cn.

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.

Based on the greenhouse gas inventory guidelines proposed by the Intergovernmental Panel on Climate Change of the United Nations， the carbon emissions of Heilongjiang Province were calculated from aspects such as energy， industrial production processes， and land use. The correlation between different factors and carbon emissions was analyzed using grey relational theory and Tapio decoupling theory， and the STIRPAT model was used to explore the differences in the impact of various factors on carbon emissions. Future carbon emissions in Heilongjiang Province were predicted using scenario analysis. The results showed that： ① The total carbon emissions in Heilongjiang Province grew rapidly from 2005 to 2012， then showed a fluctuating downward trend from 2012 to 2020， with the decline gradually slowing. ② Energy carbon emissions dominated the carbon emissions in Heilongjiang Province， accounting for approximately 90%， far exceeding the other three categories of carbon emissions. ③ The order of influence of different factors on carbon emissions was as follows： land area > population > energy structure > wealth level > energy intensity. ④ During the study period， carbon emissions in Heilongjiang Province reached the peak in 2012 according to the STIRPAT model， effectively achieving the goal of "carbon peak." ⑤ In the scenario simulation， the optimal scenario was Scenario IV. In this scenario， the rate of change for each factor was based on the more optimal values between the recent actual data from Heilongjiang Province and the "14th Five-Year Plan."

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large and medium-scale statistics, but at smaller scales (district and county level), it still remains unclear. Due to the high correlation between nighttime light (NTL) data and ECCE, this study combines “energy inventory statistics” with NTL data to estimate ECCE at smaller scales. First, we obtained city-level statistics on ECCE and corrected the NTL data by applying the VANUI index to the original NTL data from NPP-VIIRS. Second, an analysis was conducted on the correlation between the two variables, and a model was created to fit the relationship between them. Under the assumption that ECCE will be consistent within a given region, we utilized the model to estimate ECCE in districts and counties, eventually obtaining correct results at the county-level. We estimated the ECCE in each district and county of Jiangsu Province from 2013 to 2022 using the above-proposed approach, and we examined the variations in these emissions both spatially and temporally across the districts and counties. The results revealed a significant degree of correlation between the two variables, with the R2 of the fitting models exceeding 0.8. Furthermore, ECCE in Jiangsu Province fluctuated upward during this period, with clear regional clustering characteristics. The study’s conclusions provide information about how carbon emissions from small-scale energy use are estimated. They also serve as a foundation for the creation of regional energy conservation and emission reduction policies, as well as a small-scale assessment of the present state.

Analysis of carbon emissions in urban building sector using multi-influence model

The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data