Factors Affecting Carbon Emissions Research Articles

Research on Factors Affecting Carbon Emission

There are many factors affecting carbon emissions, involving energy consumption, industrial production, transportation and other aspects. In-depth study of these factors will not only help us understand the growth mechanism of carbon emissions, but also provide scientific basis for formulating effective emission reduction strategies. On the road of pursuing sustainable development, reducing carbon emissions and protecting the earth has become our common responsibility. In this paper, x indicators related to regional carbon emissions, economy, population and energy consumption and corresponding data are established to establish an indicator system. Secondly, the data is prepossessed before the establishment of the grey correlation model, which mainly includes the principal component analysis of the data and the selection of four indicators with large index scores. Finally, the grey correlation model is established to analyze the changes among related indicators and draw the conclusion of current situation analysis.

Impacts of spatial and temporal patterns and economic development on carbon emissions in Chinese cities

With the economic and social development and the improvement of peoples living standards, the urban carbon emission increases. Studying the influence of industrial structure and spatial patterns on carbon emission under the economic development of different cities can help to find the main factors affecting carbon emission, formulate policies to solve the problem according to local conditions, and provide an important scientific basis for the transformation of economic growth mode and the construction of low-carbon cities. This paper draws conclusions by analyzing the trend curves of urban carbon emissions and economic factors over the years. Different regions of the citys economic development potential differences are obvious, and the potential for greater spatial concentration of cities; citys economic development potential in the spatial and temporal distribution of key cities and major urban agglomerations as the basis for the point - line - surface in order to promote. economically developed cities, the total carbon emissions, carbon emissions growth rate of a declining trend; economically underdeveloped areas, the total carbon emissions, carbon emissions growth rate of a year-on-year trend; the degree of carbon emissions and the degree of prosperity of the secondary industry is closely linked. Chinas urban carbon emissions have a significant positive spatial correlation between 2006 and 2016, the cold spot area of urban carbon emissions is relatively stable, mainly distributed in the eastern and southern economic zones, while the hot spot area is mainly distributed in the northwest, northeast and the middle reaches of the Yellow River economic zone.

The Prediction of Medium- and Long-Term Trends in Urban Carbon Emissions Based on an ARIMA-BPNN Combination Model

Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a novel model based on Lasso regression, an ARIMA model, and a BPNN is proposed. Lasso regression is used to screen the key factors affecting carbon emissions, and the ARIMA model is used to extract the linear components of the carbon emission sequences, while the BPNN is used to predict the residuals of the ARIMA model. The final result is the sum of that from the ARIMA model and the BPNN. The carbon peak, carbon neutralization time, and emissions were analyzed under different scenarios. Taking Suzhou City as an example, the results show that the electricity consumption of the whole population is one of the key drivers of carbon emissions; the carbon emission prediction accuracy and stability of the ARIMA-BPNN combined model are better than those of the single model, which improves the reliability as well as the accuracy of the model’s prediction. However, under the constraints of the current policies, the goal of achieving carbon peaking by 2030 in Suzhou City may not be realized as scheduled. This novel carbon emission prediction model built was validated to provide a scientific basis for low-carbon urban development. This study presents an important reference value for predicting carbon emissions and formulating emission reduction measures in other cities.

Evolution characteristics, carbon emission effects and influencing factors of production-living-ecological space in Taihang Mountain poverty belt, China

The rapid advancement of urbanization and industrialization in China has gradually spread to the poor mountainous areas, which has not only brought about rapid economic development but has also caused the increasing competition for production-living-ecological spaces (PLES) and many ecological and environmental problems, carbon emissions have also increased. As an economically less developed and ecologically fragile area in China, whether the transition of the PLES in the mountain poverty belt has unique characteristics? How the PLES transition in mountainous areas affects carbon emissions and what are the important factors affecting carbon emissions? To explore these issues in depth, we studied the Taihang Mountain area in Shijiazhuang (TMS) using remote sensing image interpretation data from 2000, 2010, and 2020, and we analyzed the PLES evolution characteristics, carbon emission changes, carbon emission effects and its influencing factors of PLES. The results are as follows: 1) The TMS was dominated by ecological and production space. From 2000 to 2020, the production space decreased by 384.66 km2, the ecological space increased by 123.80 km2, and the living space increased by 260.86 km2. Agricultural production space was mainly converted to ecological and rural living space. Industrial and mining productive space was mainly converted to agricultural productive space and urban living space. 2) The study area was in a state of carbon deficit, the transition of ecological space and agricultural productive space to industrial and mining productive space and living space were the main transition types caused the carbon emissions increasing, and that of industrial and mining productive space to agricultural productive space was the main type caused the carbon emissions decreasing. 3) The proportion of construction land, urbanization rate and proportion of secondary industry are the main factors leading to the increase of carbon emissions. Per capita energy consumption, forest coverage and proportion of tertiary industry are the main factors leading to the decrease of carbon emissions. This can provide new ideas for research on carbon emissions from land-use changes and a theoretical basis for the optimization of territorial space in the mountainous areas of China.

Research on carbon emission efficiency and spatial-temporal factors in the transportation industry: Evidence from the Yangtze River Economic Belt

<p>Severe climate problems have forced the Chinese government to put forward the goal of "carbon peak" and "carbon neutrality". The transport sector is a key area for carbon reduction. Based on this background, this paper constructs the Super-SBM model and the Malmquist-Luenberger index model containing the undesirable output. This paper measures the carbon emission efficiency of transport industry in China's Yangtze River Economic Belt from static and dynamic perspectives during 2014-2020. Finally, the GTWR model is constructed to analyze the factors affecting carbon emission efficiency. The results show that: (1) the carbon emission efficiency of provinces in the Yangtze River Economic Belt is characterized by heterogeneity. Hubei was the highest, while Shanghai, Hunan, Sichuan and Yunnan were below average. (2) According to the Malmquist-Luenberger index analysis, the transportation carbon emission efficiency of the Yangtze River Economic Belt has a downward trend. Over the 2014-2020 period, carbon efficiency will decline by an average of 7% per year. (3) Energy consumption structure, industrial upgrading, economic development and population agglomeration have significant effects on carbon emission efficiency.</p>

Analysis of Factors Affecting Carbon Emissions based on Gray Correlation Analysis

In order to explore the influencing factors of carbon dioxide emissions, this paper collects data on carbon emissions and several influencing factors that may be related to its changes from 2004 to 2021, considering that too many indicators may make the data redundant and lack of representativeness, using the K-Means clustering algorithm to classify the indicators and defining the names of the corresponding first-level indicators, and analyzing the elbow diagrams for the determination of the model K-value. For the determination of the K value of the model, the optimal number of clusters was analyzed using the elbow diagram, so that the center of each cluster as the first-level indicator data was spliced with the normalized emission data to form a new dataset, and the correlation coefficients between the first-level indicators and the carbon emissions were finally calculated using the grey correlation analysis method. The results show that the ability to mitigate carbon dioxide content has the greatest impact on carbon content, and the correlation coefficient between the two reaches 0.756, which indicates that increasing the green area is very effective in achieving the goal of energy saving and emission reduction.

Differential Quantitative Analysis of Carbon Emission Efficiency of Gansu Manufacturing Industry in 2030

Decomposition analysis and forecasting of carbon emissions in manufacturing are crucial for setting sustainable carbon-reduction targets. Given the varied carbon-emission efficiencies across sectors, this study applied the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze the drivers of carbon emissions in Gansu’s manufacturing sector, encompassing high, medium, and low-efficiency industries, and it identified vital factors affecting carbon emissions. A localized Long-range Energy Alternatives Planning System (LEAP) model for Gansu was also developed. This model includes six developmental scenarios to project future carbon emissions. The study results are as follows: (1) LMDI decomposition indicates that increased carbon emissions in the manufacturing industry primarily result from economic growth in less efficient sectors and the dominance of moderately efficient ones. (2) Under Optimization Scenario 6, a 50.82 × 104 ton reduction in carbon emissions is projected for Gansu’s manufacturing sector by 2030 compared to 2020, marking the carbon peak. These outcomes provide valuable insights for policy reforms in Gansu’s manufacturing industry, aiming for carbon peaking by 2030.

Deep Learning-Based Carbon Emission Forecasting and Peak Carbon Pathways in China’s Logistics Industry

As a major energy-consuming industry, energy conservation and emission reduction in the logistics industry are critical to China’s timely achievement of its dual-carbon goals of “carbon peaking” by 2030 and “carbon neutrality” by 2060. Based on deep learning, Random Forest (RF) was used to screen out the key factors affecting carbon emissions in the logistics industry, and the Whale Algorithm-optimized Radial Basis Function Neural Network (WOA-RBF) was proposed. The Monte Carlo simulation predicted the future evolution trends of each key factor under the three scenarios of baseline scenario (BAU), policy regulation scenario (PR), and technological breakthrough scenario (TB) and accurately predicted the carbon emission trends of the logistics industry from 2023 to 2035 by using the most probable future values of each influencing factor as inputs to the WOA-RBF prediction model. The results of the study demonstrate that fixed asset investment (LFI), population (P), total energy consumption (E), energy consumption per unit of value added of the logistics industry (EIL), share of oil consumption (OR), and share of railway freight turnover (RTR) are the key factors influencing the logistics industry’s carbon emissions. Monte Carlo simulations can effectively reflect the uncertainty of future changes in these key factors. In comparison to the BAU and PR scenarios, the TB scenario, with the combined incentives of national policy regulation and technology innovation, is the most likely for the logistics industry to meet the “Peak Carbon” goal baseline scenario.

Research on Optimization of Agricultural Products Cold Chain Logistics Distribution System Based on Low Carbon Perspective

Based on thindiscussion of the traditional agricultural product distribution model, this article establishes a low-carbon perspective of urban agricultural product co-distribution model to reduce the level of agricultural product circulation and reduce the impact of distribution activities on the environment. By comprehensively considering the factors affecting carbon emissions in the vehicle delivery process, the fuel consumption and carbon emissions estimation models of delivery vehicles are analyzed and put forward. A mathematical model for the optimization of urban agricultural product cold chain distribution routes from a low-carbon perspective is established. This article takes a logistics center as an example, selects genetic algorithm as the model solution method, optimizes the distribution route, and obtains the corresponding result, thus verifying the rationality and feasibility of the model.

Beyond the numbers: unveiling the environmental impacts of international tourism and the role of renewable energy transition

ABSTRACT In pursuit of Saudi Arabia's Vision 2030 objectives for a sustainable and low-carbon future, this study examines the intricate dynamics between carbon emissions and key variables crucial to the nation's decarbonisation strategy. Grounded in the Triple Bottom Line (TBL) Theory, the chosen variables – sustainable tourism, renewable energy demand, and natural resource management – take centre stage. Applying Markov switching regime regression to data spanning 1990–2022, our findings reveal a positive association between international tourism and carbon emissions, aligning with the Tourism-induced Environmental Impact theory. Incorporating the Environmental Kuznets Curve (EKC), our results illustrate a U-shaped relationship. Renewable energy consumption (REC) exhibits a negative impact, supporting EKC's premise that economic development and cleaner energy adoption mitigate environmental degradation. Identified as influential factors affecting carbon emissions, Economic growth and natural resource capital find theoretical backing. The second power of economic expansion positively influences carbon emissions, while the square of natural resources has a negative impact, aligning with resource scarcity theories. Granger causality estimates validate Tourism-led Growth, Resource Capital, and Emissions-led Growth hypotheses. Integrating insights from the Community-Based Tourism (CBT) and stakeholder theory, this study provides actionable insights for policymakers steering Saudi Arabia toward a sustainable, low-carbon future.

Carbon Emission Characteristics and Influencing Factors of Typical Processes in Drinking Water Treatment Plant

Based on the actual operating conditions and data monitoring, the carbon emission characteristics of typical processes of a drinking water treatment plant (DWTP) in Tianjin were studied. The total carbon emission intensity measured by CO2-eq was 0.254 kg·m-3, and the proportion of carbon emissions from electricity consumption and reagent consumption was 81.76% and 9.15%, respectively. The key carbon emission sectors of electricity consumption were the water supply pump house, ultrafiltration membrane process, and inlet pump house, which accounted for 50.99%-73.51%, 17.64%-20.70%, and 17.97%-22.40% of the total carbon emission from electricity consumption in the DWTP, respectively. The contribution of sodium hypochlorite to the carbon emission of reagent consumption was 89.12%-90.30%, followed by ferric chloride, PAC, and ammonium sulfate. In the traditional water purification process, the carbon emission intensity of the process unit was in the order of inlet pump house > rapid filter > sedimentation tank. The order in the ultrafiltration membrane advanced treatment process was inlet pump house > ultrafiltration membrane > mechanical coagulation > clarification tank. The carbon emission intensity of the rapid filter process and the ultrafiltration membrane process were 0.070 9 kg·m-3 and 0.109 0 kg·m-3, respectively. The ultrafiltration membrane process could save 23% of the reagent consumption, and its carbon emission of electricity consumption was twice that of the traditional treatment process. The analysis of factors affecting carbon emission in key sectors showed that the raw water quality parameters such as turbidity, pH, ammonia nitrogen, temperature, etc., were significantly correlated with the carbon emission intensity of sodium hypochlorite. There was a significant linear regression relationship between ex-factory water pressure, daily water supply, and carbon emission intensity of the water supply pump house. The control measures of water quality and water pressure can effectively reduce the carbon emissions of the DWTP.

Carbon Emission and Industrial Structure Adjustment in the Yellow River Basin of China: Based on the LMDI Decomposition Model

In the context of promoting high-quality development in the Yellow River Basin (YRB) of China, urgent action is needed to achieve the “Dual Carbon” goal through energy savings, emission reductions, and industrial upgrading. This study measures carbon emissions from eight types of energy consumption across 43 industries from 2000 to 2019. Using the Kaya-LMDI model, factors affecting carbon emissions are analyzed, and the relationship between industrial structure and carbon emissions is explored through the coefficient of variation (CV). The findings reveal that coal consumption remains significantly higher than other energy sources, and the effect of energy structure adjustment on carbon emission reduction is limited compared to the impact of energy consumption increase on carbon emission growth. Moreover, the economic output effect is identified as the primary driving factor of carbon emissions, while energy utilization rate is crucial in achieving energy savings and emission reductions. Finally, the CV of carbon emissions across 43 industries is increasing. Based on these results, we suggest several policy recommendations, including prioritizing ecological concerns, developing comprehensive and scientifically sound plans, optimizing energy consumption structure, improving energy utilization efficiency, and adjusting industrial structure to promote sustainable development in the YRB.

Forecasting urban carbon emissions using an Adaboost-STIRPAT model

Solving outstanding environmental issues, reducing carbon emissions, and promoting green development are necessary ways to achieve carbon neutrality and carbon peak goals. It is also an important issue faced by society today. This paper uses the Kaya identity combined with the logarithmic mean Divisia index (LMDI) decomposition method to analyze the factors affecting carbon emissions, and uses the Pearson correlation coefficient to screen out eight highly correlated features to construct an extended STIRPAT model. In order to further improve the accuracy of the model in predicting carbon emissions, this paper introduces the Adaboost algorithm from machine learning to enhance the STIRPAT model. Finally, scenario analysis is used to predict and analyze carbon emissions in Shandong Province from 2020 to 2050. The results show that: 1) The main factors affecting urban carbon emissions from 1998 to 2019 are economic growth effects, followed by energy structure effects and energy consumption effects. 2) Under three different development scenarios, Shandong Province can achieve carbon peak between 2030–2035, but there are differences in peaking time and peak values.

Analysis of the Evolution Law of China's Economic Growth and Carbon Emissions

Global CO2 emissions are on the rise, thus governments are increasingly concerned about carbon emissions. As the trend of China's carbon emissions is also changing, previous studies have looked for factors affecting carbon emissions, including energy and economy, etc. This paper uses the Environmental Kuznets Curve (EKC) theory to have a descriptive statistical analysis of carbon emissions and related economic indicators in mainland China. In the period that follows, China's energy demand potential is huge, but it also faces a severe CO₂ emission reduction challenge. Improving energy efficiency and optimizing energy mix are effective solutions to China's carbon emission problem. China still has a long way to go in industrializing its economy and strengthening the structural transformation of the service sector, characterized by rapid development and huge energy demand. This study will help us to understand the evolution of carbon emissions in China and provide guidance for future carbon emission management and mitigation. Further research and actions to improve energy efficiency, adjust industrial structure and promote clean energy development are important for reducing China's carbon emissions.

Revealing the spatio-temporal characteristics and impact mechanism of carbon emission in China's urban agglomerations

Addressing carbon emissions is central to tackling climate change and achieving Sustainable Development Goals (SDGs). In this context, urban agglomerations emerge as crucial players. Our study delves into the carbon emission trends and their driving mechanisms across 19 Chinese urban agglomerations from 2000 to 2018 by spatial statistical analysis and GeoDetector. We found that while total carbon emissions have been on the rise, the rate of increase has decelerated. A regional disparity is evident in the carbon emissions of these 19 urban agglomerations, characterized by higher emissions in the east and lower in the west. Notably, carbon emissions demonstrate considerable concentration in urban agglomerations with higher levels of development and their major cities. Regarding the factors affecting carbon emissions growth, economic scale (E) stands out as the most dominant, whereas carbon intensity (CI) and industrial structure (I) have a more subdued impact. These factors, when interacting, have a compounded effect compared to their individual influences. We advocate for a stratified carbon reduction approach centered on urban agglomerations, complemented by a bespoke strategy for core cities. Furthermore, carbon reduction efforts in urban agglomerations should carefully consider the interactive influence of multiple factors.

Calculation of carbon emission efficiency in China and analysis of influencing factors.

Carbon emissions have risen in line with China's economic expansion. The key to sustainable development is finding a way to strike a balance between economic expansion and environmental protection, so improving carbon emission efficiency is vital. This paper uses provincial data from 2010 to 2020 to account for total carbon emissions using the emission factor method and obtains carbon emission efficiency data on this basis. A dynamic spatial Durbin model is then used to empirically test the possible influencing factors. The results show that, firstly, the growth rate of total carbon emissions is generally in line with the growth rate of GDP, indicating that there is no 'decoupling' in the economic system. Second, regional carbon emissions and carbon emission efficiency are not necessarily related. Thirdly, there is a clear spatial effect on carbon emission efficiency. The eastern region has the highest carbon emission efficiency, the western region has the lowest, and the northeastern and central regions have little difference in carbon emission efficiency. Further spatial and temporal migration analysis reveals that five provinces have made the migration between 2010 and 2020. Fourthly, in the short term, the direct and indirect effects of the factors affecting carbon emission efficiency are insignificant, but in the long term, most of the factors have significant direct and indirect effects on carbon emission efficiency. Finally, based on the above research findings, this paper makes policy recommendations.

Carbon emission reduction potential and reduction strategy of China's manufacturing industry

China's carbon reduction targets have been hampered primarily by excessive CO2 emissions from manufacturing. Considering technology heterogeneity, this study first employs the directional distance function (DDF) meta-frontier approach to evaluate the manufacturing carbon emission efficiency and carbon reduction potential in 29 Chinese provinces from 2011 to 2019. We also discuss the influencing factors affecting carbon emission efficiency using the spatial econometric model. The results indicate that: (1) China's manufacturing industry is not very efficient in terms of carbon emissions. The Eastern region exhibits a significantly higher level of performance than the Central and Western regions in carbon emission efficiency and production technology, although the gap is gradually narrowing. (2) The potential for reducing carbon emissions in China's manufacturing sector is considerable, with the greatest potential found in the Western region. Specifically, in the Eastern region, the carbon reduction potential is largely associated with management inefficiency. However, in the Central and Western regions, excessive carbon emissions are mainly due to technology gap inefficiency. (3) Through spatial econometric analysis, we find that carbon emission efficiency can be derived by adjusting the industrial structure, scaling up enterprises, accelerating urbanization, and reducing coal use. But it will be hindered by increased marketization and strict environmental regulations. Besides, China is not enough to improve carbon emission efficiency through technology introduction and technological innovation at present. Some manufacturing carbon emission reduction strategies are proposed based on the results of this study.

Impact of Green Finance on Carbon Emissions Based on a Two-Stage LMDI Decomposition Method

As the “double carbon” goal gains traction worldwide, achieving a balance between economic growth and environmental sustainability has become a focal point for researchers. Green finance, as a specialized financial service, offers a new approach to sustainable development. This study analyzed carbon emission sources in 25 Chinese provinces between 2007 and 2020, dividing them into energy structure, economic development, energy efficiency, and industrial structure, using a two-stage LMDI method. It then examines the linear and non-linear relationships between green finance and carbon emissions using spatial measurement methods. The findings indicate that green finance is an effective way to reduce carbon emissions, primarily through its impact on energy structure, energy efficiency, and industrial structure. There are significant non-linear relationships between green finance and the factors affecting carbon emissions, with spatial effects playing a significant role in carbon emissions influenced by energy structure, economic development, energy efficiency, and industrial structure.

Synergistic effect of energy and industrial structures on carbon emissions in China

Energy structure and industrial structure are two crucial economic factors affecting carbon emissions. However, current research often examines them separately, neglecting the potential additional synergistic effect between them. Leveraging the coupling concept from physics, we objectively quantify these synergistic effect and investigate influencing factors on CO2 intensity from a novel perspective of the synergy by combining a coupling coordination model with econometric model of generalized method of moments (GMM) with a panel dataset from China spanning 2007 to 2019. Our estimates indicate that (1) synergy of energy and industrial structures significantly reduces carbon intensity, which is stable after a series of robust check. (2) the reduced effect of synergy can be enlarged by enhancing environmental regulation and green innovation. (3) the inhibiting effect of synergy is significant, mainly occurs in regions with abundant energy resource endowments. Correspondingly, we recommend several policy implications for China and other developing countries.

Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40–110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.