A Comparative Study about Carbon Emissions

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.

Based on the time series decomposition of the Log-Mean Divisia Index, this paper analyzes the driving factors of carbon emissions from energy consumption by introducing the indicators of energy trade in China during the period of 2000–2014. The carbon emissions are decomposed into carbon emission coefficient, population, economic output, energy intensity, energy trade, energy structure and industrial structure effect in the manuscript. The result indicates that economic activity has the largest positive effect on the variation of carbon emissions. The energy trade has a greatest opposite effect on carbon emission change. At the same time, China has achieved a considerable decrease in carbon emission mainly due to the improvement of energy intensity and the optimization of energy and industrial structure. However, the influences of those changes in energy intensity, energy and industrial structure are relatively small. In addition, through the analysis by using a suitable index of energy trade, it was found that improving the conditions of energy trade can effectively optimize the energy structure and reduce the carbon emission in China.

In order to cope with global warming, China has put forward the “30 · 60” plan. We take Henan Province as an example to explore the accessibility of the plan. Tapio decoupling model is used to discuss the relationship between carbon emissions and economy in Henan Province. The influence factors of carbon emissions in Henan Province were studied by using STIRPAT extended model and ridge regression method, and the carbon emission prediction equation was obtained. On this basis, the standard development scenario, low-carbon development scenario, and high-speed development scenario are set according to the economic development model to analyze and predict the carbon emissions of Henan Province from 2020 to 2040. The results show that energy intensity effect and energy structure effect can promote the optimization of the relationship between economy and carbon emissions in Henan Province. Energy structure and carbon emission intensity have a significant negative impact on carbon emissions, while industrial structure has a significant positive impact on carbon emissions. Henan Province can achieve the “carbon peak” goal by 2030 years under the standard and low-carbon development scenario, but it cannot achieve this goal under the high-speed development scenario. Therefore, in order to achieve the goals of “carbon peaking” and “carbon neutralization” as scheduled, Henan Province must adjust its industrial structure, optimize its energy consumption structure, improve energy efficiency, and reduce energy intensity.

<p style="text-indent:20.0pt"><span style="font-size:10.5pt"><span style="font-family:"Times New Roman",serif"><span style="color:black"><span class="fontstyle01" style="font-family:仿宋"><span style="color:black"><span lang="EN-US" style="font-size:10.0pt"> High energy consumption industry is an important source of carbon dioxide emissions, and reducing pollution and carbon is an important measure for China to achieve the goal of "2030 carbon peak 2060 carbon neutral". Based on the improvement of the traditional calculation method of IPCC carbon emission intensity, this paper measures the carbon emission intensity, selects the data of high energy consuming industries in 30 provinces in China from 1997 to 2022 as samples, uses the stipat model and Moran index to analyze the correlation of the influencing factors of carbon emission, and uses the spatial measurement model to study the spatial effect of carbon emission intensity. The results show that: first, the overall carbon emission intensity of high energy consuming industries shows a downward trend, with typical spatial heterogeneity. During the sample period, the carbon emission intensity of high energy consuming industries in 30 provinces in China was calculated based on the IPPC method, with the overall decline. Second, the carbon emission intensity of high energy consuming industries has significant spatial autocorrelation characteristics. According to the global Moran index, the center of gravity moves from east to West as a whole. Third, the carbon emission intensity of high energy consuming industries is affected by multiple environmental factors. Industrial structure (INS), regional gross domestic product (GDP) and regional economic development (ECO) have a significant impact. Fourth, the carbon emission intensity of high energy consuming industries has a significant spatial spillover effect. According to the regression results of spatial Dobbin model with double fixed effects, the direct and indirect effects of carbon emission intensity of high energy consuming industries are significant.</span></span></span></span></span></span></p>

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.

Analysis on the influencing factors of carbon emission in China's logistics industry based on LMDI method

In this paper, we study the radial neural network algorithm for low‐carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. After an in‐depth analysis of the particle swarm algorithm, an improved particle swarm algorithm is proposed to improve the search accuracy and capability of the algorithm by nonlinearly adjusting the inertia weights and introducing the average extreme value factor, in response to the problems of premature convergence and poor search capability that appear in the particle swarm algorithm. Through the analysis and evaluation of the interaction between industrial ecosystem and carbon emission, the main influencing factors of carbon emission are identified, and the size and magnitude of the influence of economic growth, industrial structure, energy intensity, and energy structure on carbon emission are determined; the current situation of the industrial ecological structure is evaluated, and the direction of optimization and adjustment of industrial economic structure, energy structure, and ecological structure is clarified. We construct a multidimensional multiconstraint multimodel industrial ecological structure optimization prediction model, set the development scenarios of economy and society, and optimize the prediction of low‐carbon industrial ecological structure in forest areas; based on the simulation analysis of the prediction results, we propose the direction of industrial ecological structure adjustment and the path of industrial ecological system construction.

In 2020, China promised to achieve carbon peaking by 2030 and carbon neutrality by 2060, and these targets are famous as "Goal 3060" in China. Chinese resource-based cities are concerned about the realization of Goal 3060 to practice national action against environmental change. In this paper, this study evaluates the impact of population, economic growth, energy intensity, industrial structure, fixed asset investment, and urbanization level on carbon emissions in Chinese cities. To do so, the paper divides 36 Chinese cities into four types (growing city, mature city, recessionary city, and regenerative city) from 2003 to 2017 by factor investigation according to the diverse development stages. The extended STIRPAT model is used to assess the impact of various factors on CO2 emissions in the Yellow River basin and diverse city levels. The panel regression analysis was conducted for the basin as a whole and cities at different development stages through a fixed-effects model and a linear regression model with Driscoll-Kraay standard errors. The results show that (1) the total carbon emissions in the Yellow River basin continued to climb during the study period. However, the growth rate slowed down significantly after 2012. In addition, there are differences in the total carbon emissions and growth rate of different cities. (2) Population, real GDP, energy intensity, industrial structure, and fixed asset investment all have a significant positive impact on carbon emissions in the overall basin except the urbanization level which has a significant negative influence on carbon emissions. (3) There is heterogeneity in the influencing factors of carbon emissions in resource-based cities at various development stages. Based on these results, corresponding policies are proposed for different types of cities to help resource-based cities achieve the 3060 dual carbon goal.

Carbon emissions are the most direct and effective indicators to measure the level of low-carbon economy development. The influencing factors of carbon emissions can be decomposed into carbon emissions intensity effects, industrial structure effects, economic development effects, and population growth effects. This study based on the LMDI model, the factors which affected the carbon emissions changes of Henan province, was decomposed from 1990 to 2010. The results show that it is still at a relatively high carbon development stage in Henan Province, the pressure to realize low-carbon development is comparatively large. The direction and strength of the carbon emissions influencing factors were relatively largely different and in continuous dynamic changes. On the whole, the economic development is the main factor to promote the growth of carbon emissions and has contributed the most to the carbon emissions growth; Population growth also played a certain role in promoting the growth of carbon emissions, but is relatively limited. Carbon emission intensity changes are the main factors that inhibit the growth of carbon emissions and play the most significant role in reducing carbon emissions. The adjustment of industrial structure has limited effects on carbon emissions. It is the most effective way to realize low-carbon development, such as changing the energy structure, improving the industrial structure, and thereby reducing the carbon emissions.

Global increasing carbon emissions have triggered a series of environmental problems and greatly affected the production and living of human beings. This study estimated carbon emissions from land use change in the Beijing-Tianjin-Hebei region during 1990–2020 with the carbon emission model and explored major influencing factors of carbon emissions with the Logarithmic Mean Divisia Index (LMDI) model. The results suggested that the cropland decreased most significantly, while the built-up area increased significantly due to accelerated urbanization. The total carbon emissions in the study area increased remarkably from 112.86 million tons in 1990 to 525.30 million tons in 2020, and the built-up area was the main carbon source, of which the carbon emissions increased by 370.37%. Forest land accounted for 83.58–89.56% of the total carbon absorption but still failed to offset the carbon emission of the built-up area. Carbon emissions were influenced by various factors, and the results of this study suggested that the gross domestic product (GDP) per capita contributed most to the increase of carbon emissions in the study area, resulting in a cumulative increase of carbon emissions by 9.48 million tons, followed by the land use structure, carbon emission intensity per unit of land, and population size. By contrast, the land use intensity per unit of GDP had a restraining effect on carbon emissions, making the cumulative carbon emissions decrease by 103.26 million tons. This study accurately revealed the variation of net carbon emissions from land use change and the effects of influencing factors of carbon emissions from land use change in the Beijing-Tianjin-Hebei region, which can provide a firm scientific basis for improving the regional land use planning and for promoting the low-carbon economic development of the Beijing-Tianjin-Hebei region.

Under the "dual carbon" goal， promoting energy conservation and emission reduction is the key to high-quality economic development. Through innovative analysis， we aim to analyze and predict the influencing factors of carbon emissions in Jiangsu Province from multiple dimensions and provide targeted strategies to reduce carbon emissions. Based on the STRIPAT extended model and LMDI model， we construct an index system of influencing factors of carbon emissions in Jiangsu Province and analyze the impact of different indicators on carbon emissions from multiple dimensions. Using ridge regression and factor analysis methods， we obtain the correlation and contribution rate between carbon emissions and various indicators and predict the carbon emissions in Jiangsu Province using the BP neural network algorithm. The results showed that the ranking of the influencing factors of carbon emissions in Jiangsu Province was： energy consumption， GDP， population， proportion of added value of the tertiary industry， energy consumption structure， proportion of added value of the secondary industry， and proportion of added value of the primary industry. Among them， the proportion of added value of the primary industry and the proportion of added value of the secondary industry had a restraining effect on the growth of carbon emissions， while the remaining factors had a promoting effect. At the same time， according to the prediction results， Jiangsu Province should adjust its industrial and energy structure between 2025 and 2035， increasing the proportion of non-fossil energy to 30%， reducing unit CO2 emissions by 28.6%， and achieving carbon peak. Around 2050， increasing the proportion of non-fossil energy to 50% and reducing unit energy consumption by 46.1% will lead to a rapid decline in CO2 emissions. Eventually， around 2060， the proportion of non-fossil energy will exceed 80%， unit energy consumption will decrease by 54.6%， and CO2 emissions will decrease by 77.9%， achieving carbon neutrality.

Achieving carbon peak before 2030 and carbon neutrality before 2060 is a solemn commitment made by China to address global climate change, and is also one of the main goals for economic and social development in the 14th Five Year Plan and the 2035 vision period. The changes in carbon emissions are directly related to the progress of China's "carbon peak" and "carbon neutrality" goals. Therefore, in-depth research on the influencing factors of carbon emissions has become a key link in promoting the achievement of this goal. In the existing research on carbon emission influencing factors, countries mainly focus on macro scale low-carbon urban carbon emission influencing factors and micro scale low-carbon building full life cycle carbon emission influencing factors. However, there is relatively little research on the influencing factors of carbon emissions in low-carbon communities of urban micro units, and there is still considerable research space. This study conducted an in-depth analysis of the influencing factors of carbon emissions in low-carbon communities using complex network methods. By constructing a complex network model of factors affecting carbon emissions, we identified key nodes and pathways, and explored their interrelationships. The results indicate that factors such as energy structure, resident behavior, building design, and policy implementation play an important role in carbon emissions in low-carbon communities.

This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.

Taking the panel data of 78 cities above the prefecture-level in East China from 2003 to 2017 as a sample, the STIRPAT model is used to empirically analyze the factors affecting carbon emissions in East China. The empirical results find that the carbon emission intensity, population, industrial structure, and innovation level in East China have a positive driving effect on carbon emissions, while education investment level, the level of actual use of foreign capital, and financial development have a depressing effect on carbon emissions. There is an “inverted U”-shaped nonlinear relationship between economic development level and carbon emissions. With the improvement of economic development level, carbon emissions show a trend of rising first and then falling after reaching a peak. Therefore, to reduce carbon emissions in East China, measures should be taken from five aspects: maintaining healthy and stable economic development, optimizing the industrial structure, actively introducing high-quality foreign investment, increasing education funding, and improving innovation capabilities.

Carbon emission is further intensified as urbanization and industrialization continue to accelerate. China has maintained its rapid economic development and urbanization in the last 2 decades. The development of the construction industry has not only consumed a large number of energy sources but also resulted in significant carbon emissions, causing some environmental damage. Recognizing the major influencing factors of carbon emissions in the construction industry has become a research hotspot to alleviate environmental pollution caused by the construction industry and meet industrial demands for energy saving and emission reduction. In this study, the factors that influence annual carbon emissions of different building types in China from 2011 to 2018 were decomposed by Logarithmic Mean Divisia Index (LMDI) through a case study in Henan Province. The major influencing factors of carbon emissions have been identified. Results demonstrate that the per capita carbon emission in the construction industry in Henan Province remains high from 2011 to 2018, but it decreases year by year. Carbon emissions from the construction industry in Henan Province increase due to economic development and energy structure. Energy efficiency can inhibit carbon emissions from the construction industry in Henan Province. The obtained conclusions have a positive effect on analyzing annual variations in carbon emissions from the construction industry in a region, identifying influencing factors, and proposing specific countermeasures of energy saving and emission reduction.