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Journal articles on the topic 'Emissions intensity'

Author: Grafiati

Published: 10 December 2022

Last updated: 26 July 2025

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1

Chontanawat, Jaruwan, Paitoon Wiboonchutikula, and Atinat Buddhivanich. "Decomposition Analysis of the Carbon Emissions of the Manufacturing and Industrial Sector in Thailand." Energies 13, no. 4 (2020): 798. http://dx.doi.org/10.3390/en13040798.

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Since the 1990s, CO2 emissions have increased steadily in line with the growth of production and the use of energy in the manufacturing sector in Thailand. The Logarithmic Mean Divisia Index Method is used for analysing the sources of changes in CO2 emissions as well as the CO2 emission intensity of the sector in 2000–2018. On average throughout the period, both the amount of CO2 emissions and the CO2 emission intensity increased each year relative to the baseline. The structural change effect (effect of changes of manufacturing production composition) reduced, but the intensity effect (effect

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2

Shen, Zijie, and Liguo Xin. "Characterizing Carbon Emissions and the Associations with Socio-Economic Development in Chinese Cities." International Journal of Environmental Research and Public Health 19, no. 21 (2022): 13786. http://dx.doi.org/10.3390/ijerph192113786.

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Reducing carbon emissions in cities is crucial for addressing climate change, while the city-level emissions of different compositions and their relationships with socio-economic features remain largely unknown in China. Here, we explored the city-level emission pattern from the industrial, transportation, and household sectors and the emission intensity, as well as their associations with socio-economic features in China, using the up-to-date (2020) CO2 emissions based on 0.1° grid (10 × 10 km) emission data. The results show that: (1) CO2 emissions from the industrial sector were considerabl

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3

QIFENG, LI, ZHANG JIANLEI, QIAN DAKE, and HE LIN. "The impact of heterogeneous environmental regulations on China's textile industry CO2 emissions." Industria Textila 74, no. 04 (2023): 419–25. http://dx.doi.org/10.35530/it.074.04.202299.

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Environmental regulation is an important tool to reduce CO2 emissions. To investigate the relationship between heterogeneous environmental regulations and CO2 emissions of China's textile industry (CTI), this paper uses the threshold model and panel model to study the impacts of command-based, market-based and public-based environmental regulations (CER, MER and PER) on the total carbon emissions and carbon emission intensity of CTI from 2004 to 2019. Then it further explores their regional heterogeneity. The results show that: at the national level, CER has a forced emission reduction effect

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4

Wang, Xinling. "International Carbon Emissions: Evolutionary Patterns, Trends, and Influencing Factors." International Journal of Energy 4, no. 3 (2024): 45–50. http://dx.doi.org/10.54097/v9r2zm71.

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This paper investigates the status and trends of global carbon emissions, including total emissions, per capita emissions, and emission intensity. It reviews the evolution and influencing factors of international carbon emissions. The increase in emissions is mainly concentrated in emerging economies and developing countries. Developed nations, despite having high total emissions, exhibit low emission intensity and stable or declining trends, often aiming for “carbon neutrality.” Emerging economies, including newly industrialized countries, are experiencing rapid increases in emissions, and ar

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5

Mrówczyńska-Kamińska, Aldona, Bartłomiej Bajan, Krzysztof Piotr Pawłowski, Natalia Genstwa, and Jagoda Zmyślona. "Greenhouse gas emissions intensity of food production systems and its determinants." PLOS ONE 16, no. 4 (2021): e0250995. http://dx.doi.org/10.1371/journal.pone.0250995.

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It is estimated that about 1/4th of all greenhouse gas (GHG) emissions may be caused by the global food system. Reducing the GHG emissions from food production is a major challenge in the context of the projected growth of the world’s population, which is increasing demand for food. In this context, the goal should be to achieve the lowest possible emission intensity of the food production system, understood as the amount of GHG emissions per unit of output. The study aimed to calculate the emission intensity of food production systems and to specify its determinants based on a panel regressio

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6

Zhang, Xiufan, and Decheng Fan. "The Spatial-Temporal Evolution of China’s Carbon Emission Intensity and the Analysis of Regional Emission Reduction Potential under the Carbon Emissions Trading Mechanism." Sustainability 14, no. 12 (2022): 7442. http://dx.doi.org/10.3390/su14127442.

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It is of great significance to study the regional differences and temporal and spatial evolution of China’s carbon emission intensity under the carbon emissions trading mechanism, and to explore the potential for regional emission reduction. This paper uses the Theil index and Moran index to analyze the regional differences and temporal and spatial evolution trend of carbon emission intensity in China from 2010 to 2019, further constructs the emission reduction effect standard of carbon emissions trading mechanisms, discusses the emission reduction effect of the trading mechanisms, and measure

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Zhu, Yong, and Congjia Huo. "The Impact of Agricultural Production Efficiency on Agricultural Carbon Emissions in China." Energies 15, no. 12 (2022): 4464. http://dx.doi.org/10.3390/en15124464.

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With the rapid development of China’s economy, China has become the world’s largest carbon emitter. China not only has an obvious growth rate of industrial carbon emissions but also the intensity of agricultural carbon emissions is hovering at a high level. The development of China’s agricultural economy has largely come at the expense of high emissions. Currently, under the background of global warming and difficulty in controlling greenhouse gas emissions, the development of low-carbon agriculture is an important way to realize the harmonious development of the ecological environment and eco

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8

Zhang, Jianqing, Haichao Yu, Keke Zhang, Liang Zhao, and Fei Fan. "Can Innovation Agglomeration Reduce Carbon Emissions? Evidence from China." International Journal of Environmental Research and Public Health 18, no. 2 (2021): 382. http://dx.doi.org/10.3390/ijerph18020382.

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Innovation agglomeration plays a decisive role in improving the input–output scale and marginal output efficiency of factors. This paper takes carbon emissions as the unexpected output and energy consumption as the input factor into the traditional output density model. The dynamic spatial panel Durbin model is used to analyze the mechanism for innovation agglomeration and energy intensity to affect carbon emissions from 2004 to 2017 in thirty Chinese provinces. Then, we test the possible mediating effect of energy intensity between innovation agglomeration and carbon emissions. The major find

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9

Fu, Liyuan, and Qing Wang. "Spatial and Temporal Distribution and the Driving Factors of Carbon Emissions from Urban Production Energy Consumption." International Journal of Environmental Research and Public Health 19, no. 19 (2022): 12441. http://dx.doi.org/10.3390/ijerph191912441.

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Urban production energy consumption produces a large amount of carbon emissions, which is an important source of global warming. This study measures the quantity and intensity of carbon emissions in 30 provinces of China based on urban production energy consumption from 2005–2019, and uses the Dagum Gini coefficient, kernel density estimation, carbon emission classification and spatial econometric model to analyze the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions from China and regional production energy consumption. It was found that the g

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10

Xinghua, WANG, WANG Qiang, GUO Xiaoli, et al. "Spatiotemporal Characteristics and Reduction Pathways of County-level Agricultural Carbon Emissions for Shanxi Province in China." RA JOURNALS OF APPLIED RESEARCH 10, no. 02 (2024): 30–43. https://doi.org/10.5281/zenodo.10725900.

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Agriculture is the main source of greenhouse gas emissions second only to energy activities and industrial production. Agricultural carbon emission reduction can effectively alleviate the negative impact of greenhouse effect. Using the emission factor method, this paper combs four types of agricultural production activities, including agricultural inputs, farmland management, animal intestinal digestion and fecal management, calculates the county-level agricultural carbon emissions quantity and intensity in Shanxi Province from 2018 to 2022, and uses GeoDa software and spatial autocorrelation

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11

Sun, Jinfang, Wenkai Li, Kaixiang Zhu, et al. "Research on Industrial CO2 Emission Intensity and Its Driving Mechanism Under China’s Dual Carbon Target." Sustainability 16, no. 23 (2024): 10785. https://doi.org/10.3390/su162310785.

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As global climate change becomes increasingly severe, industrial CO2 emissions have received increasing attention, but the impact factors and driving mechanisms of industrial CO2 emission intensity remain unclear. Based on panel data from 2010 to 2021 in Shandong Province, a key economic region in eastern China, the industrial CO2 emission intensity under China’s dual carbon target was analyzed using multivariate ordination methods. The results showed that (1) total CO2 emissions from industry are increasing annually, with an average growth rate of 3.74%, and electricity, coal, and coke are th

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12

Zhang, Zeyu. "Research on the Spatial Spillover Effect of Carbon Emission Trading on Carbon Emission Intensity." Highlights in Business, Economics and Management 45 (December 28, 2024): 1000–1008. https://doi.org/10.54097/fgdfge86.

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This article is based on panel data from 30 provinces in China from 2006 to 2019, analyzing the spatial spillover effects of carbon emission trading on carbon emission intensity through spatial econometric models. The empirical results indicate that China's carbon emission efficiency has significant regional differences and spatial correlations. Carbon emission trading can significantly reduce the carbon emission intensity of the treatment area, while also having a suppressive effect on the carbon emission intensity of surrounding areas. Based on the above conclusions, this article suggests pr

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13

Meng, Zhaosu, Huan Wang, and Baona Wang. "Empirical Analysis of Carbon Emission Accounting and Influencing Factors of Energy Consumption in China." International Journal of Environmental Research and Public Health 15, no. 11 (2018): 2467. http://dx.doi.org/10.3390/ijerph15112467.

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China is confronting great pressure to reduce carbon emissions. This study focuses on the driving factors of carbon emissions in China using the Logarithmic Mean Divisia Index (LMDI) method. Seven economic factors, including gross domestic product (GDP), investment intensity, research and development (R&D) intensity, energy intensity, research and development (R&D) efficiency, energy structure and province structure are selected and the decomposition model of influencing factors of carbon emissions in China is constructed from a sectoral perspective. The influence of various economic f

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14

Dyer, James A., Xavier P. C. Verge, Raymond L. Desjardins, and Devon E. Worth. "A Comparison of the Greenhouse Gas Emissions From the Sheep Industry With Beef Production in Canada." Sustainable Agriculture Research 3, no. 3 (2014): 65. http://dx.doi.org/10.5539/sar.v3n3p65.

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<p>Sheep production in Canada is a small industry in comparison to other livestock systems. Because of the potential for expansion of the sheep industry in Canada, the GHG emissions budget of this industry was assessed in this paper. The GHG emissions from Canadian lamb production were compared with those from the Canadian beef industry using the ULICEES model. The GHG emission intensity of the Canadian lamb industry was 21% higher than lamb production in France and Wales, and 27% higher than northern England. Enteric methane accounts for more than half of the GHG emissions from sheep in

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15

Zang, Jinrui, Xin Hu, Kun Qie, Zian Zhang, and Shi Zhang. "An EG-Tree Model Incorporating Spatial Heterogeneity for Analyzing Multifactorial Coupling Effects on Carbon Emissions Across Industries and Regions in China." Atmosphere 16, no. 6 (2025): 663. https://doi.org/10.3390/atmos16060663.

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With the proposal of the dual carbon goals, it is of great significance to identify the causes of carbon emissions and reduce carbon emissions directly. There is a lack of analysis on the causes of carbon emissions considering the coupling effect of multiple factors and regional heterogeneity. The causes of carbon emissions are examined from multiple perspectives utilizing the panel data spanning from 1997 to 2022, encompassing 30 provinces in China. To further analyze the causes of carbon emissions, an enhanced feature and regularized gradient boosting tree (EG-Tree) model is constructed, and

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16

Song, Zhaoxuan, Tingting Zhu, Shihan Yang, and Huiqin Zong. "Prediction and Analysis of Carbon Emissions under Specific Regional Scenarios in Anhui Province based on the STIRPAT Model." Journal of Innovation and Development 3, no. 2 (2023): 41–45. http://dx.doi.org/10.54097/jid.v3i2.9147.

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In order to achieve the goal of reaching carbon peak by 2030, the STIRPAT model is used to predict carbon emissions under three simulation scenarios: baseline, optimization, and strict control of carbon emissions. Taking Anhui Province as an example, fully considering the impact of factors such as population, per capita GDP, carbon emission intensity, energy consumption intensity, energy structure, and industrial structure on carbon emissions, ridge regression and partial least squares regression were conducted respectively. Finally, the partial least squares regression method with a lower ave

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17

Qin, Jiancheng, Hui Tao, Chinhsien Cheng, et al. "Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China." Sustainability 12, no. 3 (2020): 1089. http://dx.doi.org/10.3390/su12031089.

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Analyzing the driving factors of regional carbon emissions is important for achieving emissions reduction. Based on the Kaya identity and Logarithmic Mean Divisia Index method, we analyzed the effect of population, economic development, energy intensity, renewable energy penetration, and coefficient on carbon emissions during 1990–2016. Afterwards, we analyzed the contribution rate of sectors’ energy intensity effect and sectors’ economic structure effect to the entire energy intensity. The results showed that the influencing factors have different effects on carbon emissions under different s

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18

Pang, Haifeng, Changxu Wu, and Liucheng Zhang. "The impact of green bond issuance on carbon emission intensity and path analysis." PLOS ONE 19, no. 6 (2024): e0304364. http://dx.doi.org/10.1371/journal.pone.0304364.

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Reducing carbon emission intensity is crucial for achieving sustainable development. Carbon emission intensity is expressively affected by the issuance of green bonds. Thus, it is imperative to assess the influence of green bond issuance on carbon emissions and examine their correlation. Such research holds great potential to expedite the overhaul and modernization of businesses and to construct a circular economy system. This paper uses the spatial Durbin model to draw empirical conclusions by using data from 26 provinces in China between 2016 and 2021. Firstly, under different spatial matric

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19

Lin, Fengzeng, Yu Shao, Haibo Guo, Ruihong Yan, Chen Wang, and Bolun Zhao. "Carbon Emissions and Intensity of Land Use: A Rural Setting Analysis in Ningde City, China." Land 13, no. 6 (2024): 767. http://dx.doi.org/10.3390/land13060767.

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Carbon emissions and land use intensity serve as crucial indicators of land management. This paper proposes a methodological framework to elucidate the sustainability of carbon emissions in rural areas via a coordination model, scrutinizes the correlation with land use intensity, and investigates the significance of influential factors. The study focuses on village-level units within Ningde City, and finds the pronounced spatial heterogeneity characterizing the distribution of carbon emissions across different villages: (1) Villages exhibiting high levels of carbon emissions are predominantly

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20

PANDENG, SHEN, HE LIN, ZHANG JIANLEI, and CHENG LONGDI. "The impact of technological innovation from domestic innovation, import and FDI channels on carbon dioxide emissions of China's textile industry." Industria Textila 73, no. 04 (2022): 426–31. http://dx.doi.org/10.35530/it.073.04.202149.

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Technological innovation is the key to reducing carbon dioxide (CO2) emissions. In order to analyse the role of technological innovation from domestic innovation, import and FDI channels in the CO2 emissions reduction of China's textile industry (CTI), this study uses OLS models to study the impact of domestic innovation, import technology spill over and FDI technology spillover on CO2 emissions and CO2 emission intensity of CTI respectively. The research results show that domestic innovation has significantly reduced CTI’s CO2 emissions and CO2 emission intensity, while import technology spil

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21

Sazalina Zakaria, Radin Diana R. Ahmad, Ahmad Rosly Abbas, and Mohd Faizal Mohideen Batcha. "Greenhouse Gas Emission Intensity Assessment for Power Plants in Peninsular Malaysia." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 88, no. 2 (2021): 14–26. http://dx.doi.org/10.37934/arfmts.88.2.1426.

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The power sector has been playing a vital role in the industrialization, societal and economic development of a nation. In Malaysia, the total power generation for 2014 is 147,480GWh and eventually accounts for 54% of total carbon emissions for that year alone. A study was conducted to quantify the greenhouse gas emission from stationary combustion from several power plants in Peninsular Malaysia, followed by proposal for the emission reduction strategies. For the GHG emissions assessment, the Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard and Intergovernmental Panel on

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22

Cui, Jingyuan, and Yumeng Wu. "Calculation and Influencing Factors of Carbon Emissions in Countries along the Belt and Road Based on the LMDI Method." Highlights in Science, Engineering and Technology 11 (August 23, 2022): 167–76. http://dx.doi.org/10.54097/hset.v11i.1372.

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In order to study the carbon emissions of countries along the Belt and Road and its influencing factors, this paper calculates the energy carbon emissions of six major regions from 2013 to 2020 from the national level based on the LMDI index decomposition method and divides the driving factors into population, economy, industrial structure, energy intensity and carbon emission intensity, analyzing the contribution rate of each factor and regional differences. The results show that the carbon emissions of countries along the Belt and Road have shown an overall upward trend at present. The main

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23

Wang, Shangjiu, Shaohua Zhang, and Liang Cheng. "Drivers and Decoupling Effects of PM2.5 Emissions in China: An Application of the Generalized Divisia Index." International Journal of Environmental Research and Public Health 20, no. 2 (2023): 921. http://dx.doi.org/10.3390/ijerph20020921.

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Although economic growth brings abundant material wealth, it is also associated with serious PM2.5 pollution. Decoupling PM2.5 emissions from economic development is important for China’s long-term sustainable development. In this paper, the generalized Divisia index method (GDIM) is extended by introducing innovation indicators to investigate the main drivers of PM2.5 pollution in China and its four subregions from 2008 to 2017. Afterwards, a GDIM-based decoupling index is developed to examine the decoupling states between PM2.5 emissions and economic growth and to identify the main factors l

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Honghai, Yu, Wang Zhi, Chen Li, and Wu Jianan. "CO2 Emission Calculation and Emission Characteristics Analysis of Typical 600MW Coal-fired Thermal Power Unit." E3S Web of Conferences 165 (2020): 01029. http://dx.doi.org/10.1051/e3sconf/202016501029.

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In order to effectively reduce the total CO2 emissions of coal-fired power plants and reduce greenhouse gas emissions, the relevant data of a typical 600MW coal-fired power plant in the past five years was collected and investigated, and CO2 emissions and emission intensity were calculated. And the results were used to measure the CO2 emission level of coal-fired power plants. By comparing and analyzing the CO2 emission intensity and emission trend of 600MW coal-fired units with different unit types and different fuel types, the CO2 emission characteristics of typical 600MW coal-fired power pl

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25

Zhao, Yu, and Prasanna Divigalpitiya. "Understanding Emission Trends, Regional Distribution Differences, and Synergistic Emission Effects in the Transportation Sector in Terms of Social Factors and Energy Consumption." Sustainability 16, no. 24 (2024): 10971. https://doi.org/10.3390/su162410971.

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China’s transportation sector plays a significant role in reducing carbon dioxide (CO2) and air pollution. Previous studies have predominantly utilized scenario analysis to forecast emissions for the next 30 to 50 years based on coefficients from a base year. To elucidate the current state of gas emissions in the transportation sector, this study employed panel data for 10 types of gas emissions from 2001 to 2020, analyzing their emission characteristics, tendencies, and synergistic effects. Utilizing the Kaya equation and the logarithmic mean division index (LMDI) decomposition method, we dev

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Jiang, Rui, Peng Wu, and Chengke Wu. "Driving Factors behind Energy-Related Carbon Emissions in the U.S. Road Transport Sector: A Decomposition Analysis." International Journal of Environmental Research and Public Health 19, no. 4 (2022): 2321. http://dx.doi.org/10.3390/ijerph19042321.

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The U.S. is the second largest contributor to carbon emissions in the world, with its road transport sector being one of the most significant emission sources. However, few studies have been conducted on factors influencing the emissions changes for the U.S. from the perspective of passenger and freight transport. This study aimed to evaluate the carbon emissions from the U.S. road passenger and freight transport sectors, using a Logarithmic Mean Divisia Index approach. Emissions from 2008 to 2017 in the U.S. road transport sector were analysed and key findings include: (1) energy intensity an

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Huangfu, Jianhua, Wenjuan Zhao, and Lei Yu. "Does Coal Consumption Control Policy Synergistically Control Emissions and Energy Intensity?" Sustainability 15, no. 10 (2023): 7748. http://dx.doi.org/10.3390/su15107748.

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The coal consumption constraint policy (CCCP) serves a crucial role in the maintenance of environmental and economic sustainability for China. However, it is debatable whether the CCCP reduces emissions and energy intensity. The present study explores the impact and realization pathways of the CCCP on energy and emissions intensity at the city level from 2005 to 2019 using a time-varying difference-in-differences (DID) and structural equation model (SEM) approach. We find that the CCCP can control emissions and energy intensity synergistically. Particularly, the CCCP has significantly reduced

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28

Pham, Phuong M. H. "THE EFFECTS OF FINANCIAL DEVELOPMENT ON EMISSIONS: EVIDENCE FROM ASEAN COUNTRIES." Advances and Applications in Statistics 92, no. 6 (2025): 835–59. https://doi.org/10.17654/0972361725035.

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With the rapid development of industry and modern life worldwide, CO2 emissions pose a significant challenge for all countries, impacting both present and future generations. Studying the factors influencing CO2 emissions serves as a valuable tool for governments to mitigate emission levels and safeguard human lifestyles. In this context, the study is conducted to explore the relationship between financial development and CO2 emissions intensity across nine Southeast Asian Nations (ASEAN countries) from 2000 to 2021. The findings, derived from three distinct analytical methods, consistently de

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Donadia, Andrea Beltrani, Rodrigo Nazaré Santos Torres, Henrique Melo da Silva, Suziane Rodrigues Soares, Aaron Kinyu Hoshide, and André Soares de Oliveira. "Factors Affecting Enteric Emission Methane and Predictive Models for Dairy Cows." Animals 13, no. 11 (2023): 1857. http://dx.doi.org/10.3390/ani13111857.

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Enteric methane emission is the main source of greenhouse gas contribution from dairy cattle. Therefore, it is essential to evaluate drivers and develop more accurate predictive models for such emissions. In this study, we built a large and intercontinental experimental dataset to: (1) explain the effect of enteric methane emission yield (g methane/kg diet intake) and feed conversion (kg diet intake/kg milk yield) on enteric methane emission intensity (g methane/kg milk yield); (2) develop six models for predicting enteric methane emissions (g/cow/day) using animal, diet, and dry matter intake

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30

Shrestha, Prativa, and Changyou Sun. "Carbon Emission Flow and Transfer through International Trade of Forest Products." Forest Science 65, no. 4 (2019): 439–51. http://dx.doi.org/10.1093/forsci/fxz003.

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Abstract The environmental impact of commodity trade has become a considerable concern in recent decades. In this study, carbon emissions embodied in forest products trade are examined through a multiregional input–output model. Compared with other industries, the forest products industry is clean with a small total emission and mean emission intensity. The paper sector is more substantial in total emission and dirtier in emission intensity than the wood sector. Most countries with extensive forest products trade have experienced declining consumption-based carbon emissions over 1995–2009, and

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Bai, Junhong, and Xuewei Yu. "The Impact of Global Value Chain Embedment on Energy Conservation and Emissions Reduction:Theory and Empirical Evidence." China Finance and Economic Review 13, no. 2 (2024): 46–66. http://dx.doi.org/10.1515/cfer-2024-0009.

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Abstract An in-depth investigation into the effect of embedment in global value chain (GVC) on energy conservation and emissions reduction is of great significance for scientifically assessing the environmental impact of GVC participation, and promoting high-quality development in China. This paper incorporates GVC embedment, energy consumption and carbon emissions into the same analysis framework for the first time. Based on the WIOD database, this paper theoretically and empirically examines the impact and mechanism of global value chain embeddedness on carbon emission reduction from two dim

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Lunde, J., U. P. Løvhaug, and B. Gustavsson. "Particle precipitation during NEIAL events: simultaneous ground based nighttime observations at Svalbard." Annales Geophysicae 27, no. 5 (2009): 2001–10. http://dx.doi.org/10.5194/angeo-27-2001-2009.

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Abstract. In this paper we present Naturally Enhanced Ion Acoustic Lines (NEIALs) observed with the EISCAT Svalbard Radar (ESR) together with auroral emissions observed with the Meridian Scanning Photometer (MSP). This is the first report of NEIALs observed during nighttime at Svalbard. Previously, NEIALs have been associated with a strong red line intensity (>10 kR), which exceeds the green line intensities. The high intensity in the red line emission is a sign of abundant low energy electron precipitation. In our observations, one of the NEIAL events was accompanied by the red line emissi

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Ma, Chao-Qun, Jiang-Long Liu, Yi-Shuai Ren, and Yong Jiang. "The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model." Energies 12, no. 24 (2019): 4800. http://dx.doi.org/10.3390/en12244800.

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Since the reform and opening-up, China’s CO2 emissions have increased dramatically, and it has become the world’s largest CO2 emission and primary energy consumption country. The manufacturing industry is one of the biggest contributors to CO2 emission, and determining the drivers of CO2 emissions are essential for effective environmental policy. China is also a vast transition economy with great regional differences. Therefore, based on the data of China’s provincial panel from 2000 to 2013 and the improved STIRPAT model, this paper studies the impact of economic growth, foreign direct invest

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Oh, Wankeun, and Jonghyun Yoo. "Long-Term Increases and Recent Slowdowns of CO2 Emissions in Korea." Sustainability 12, no. 17 (2020): 6924. http://dx.doi.org/10.3390/su12176924.

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Korea is one of the fastest-growing CO2-emitting countries but has recently experienced a dramatic slowdown in emissions. The objective of the study is to examine the driving factors of long-term increases (1990–2015) and their slowdown (2012–2015) in emissions of Korea. This study uses an extended index decomposition analysis model that better fits Korea’s emission trends of the last 25 years by encompassing 19 energy end-use sectors (18 economic sectors and a household sector) and three energy types. The results show that emission increases in the long term (1990–2015) come from economic gro

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Christie, K. M., R. P. Rawnsley, C. Phelps, and R. J. Eckard. "Revised greenhouse-gas emissions from Australian dairy farms following application of updated methodology." Animal Production Science 58, no. 5 (2018): 937. http://dx.doi.org/10.1071/an16286.

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Every year since 1990, the Australian Federal Government has estimated national greenhouse-gas (GHG) emissions to meet Australia’s reporting commitments under the United National Framework Convention on Climate Change (UNFCCC). The National Greenhouse Gas Inventory (NGGI) methodology used to estimate Australia’s GHG emissions has altered over time, as new research data have been used to improve the inventory emission factors and algorithms, with the latest change occurring in 2015 for the 2013 reporting year. As measuring the GHG emissions on farm is expensive and time-consuming, the dairy ind

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He, Mei Ling, and Xiao Hui Wu. "Calculation and Decomposition of China’s Carbon Emissions from Transportation Energy Consumption: Based on LMDI Method." Advanced Materials Research 926-930 (May 2014): 4411–14. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.4411.

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According to the calculation method of the IPCC, the paper calculates the composition and intensity of carbon emissions from transportation energy consumption in China from 2000 to 2011. Based on logarithmic mean divisia index (LMDI) decomposition technique, changes of carbon emissions quantity are analyzed by three factors which are the transportation energy intensity, the economic growth and the transportation energy structure. The results show: (1) Transportation energy intensity was significantly decreased. Under its influence carbon emission intensity from the transportation energy was de

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WANG, Xingmin, Jing WU, Zheng WANG, Xiaoting JIA, and Bing BAI. "Accounting and Characteristics Analysis of CO2 Emissions in Chinese Cities." Chinese Journal of Urban and Environmental Studies 08, no. 01 (2020): 2050004. http://dx.doi.org/10.1142/s2345748120500049.

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Accurate estimation of CO2 emissions is a prerequisite for scientific low-carbon emission policymaking. Based on 20 types of energy consumption data at the prefecture level in China, this paper re-estimates the CO2 emissions of 198 prefecture-level cities in 2016 by using the method of carbon emission coefficient. The spatial pattern and scale characteristics are analyzed, and the conclusions are as follows: (1) Overall, China’s urban CO2 emissions show a certain degree of spatial separation in terms of the total amount, per capita emissions, and emission intensity. Cities with the highest CO2

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Filimonova, Irina, Irina Provornaya, Vasily Nemov, Anna Komarova, and Yuri Dzyuba. "Convergence of the carbon intensity of the economies of the Asia-Pacific and non-OECD countries to the level of the OECD countries." E3S Web of Conferences 265 (2021): 04022. http://dx.doi.org/10.1051/e3sconf/202126504022.

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The global goal of the world community is the transition to a “green” economy, characterized by rational use of electricity, reduction of harmful emissions, and consumption of renewable energy sources. The purpose of the research was to study the convergence of capacity emissions in developing countries to European countries’ level. According to the results, countries striving for a lower emission intensity level to varying degrees. In non-OECD European countries, per capita income growth leads to a 0.26% reduction in emissions intensity. This fact means that economic growth creates additional

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Tu, Ran, Islam Kamel, Baher Abdulhai, and Marianne Hatzopoulou. "Reducing Transportation Greenhouse Gas Emissions Through the Development of Policies Targeting High-Emitting Trips." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 25 (2018): 11–20. http://dx.doi.org/10.1177/0361198118755714.

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Traffic emission inventories have been under development for decades, often relying on data from traffic assignment models, ranging from macroscopic models generating average link speeds, to more detailed microscopic models with instantaneous speed profiles. Policy testing within such frameworks has often focused on identifying changes in total emissions, or in emissions aggregated at a zonal or street level. Emissions from specific trips or trajectories are seldom analyzed, although reductions in greenhouse gas (GHG) emissions can be achieved more efficiently when targeting high emitters. In

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Zhang, Di, Zhanqi Wang, Shicheng Li, and Hongwei Zhang. "Impact of Land Urbanization on Carbon Emissions in Urban Agglomerations of the Middle Reaches of the Yangtze River." International Journal of Environmental Research and Public Health 18, no. 4 (2021): 1403. http://dx.doi.org/10.3390/ijerph18041403.

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The urban agglomerations in the middle reaches of the Yangtze River (MYR-UA) are facing a severe challenge in reducing carbon emissions while maintaining stable economic growth and prioritizing ecological protection. The energy consumption related to land urbanization makes an important contribution to the increase in carbon emissions. In this study, an IPAT/Kaya identity model is used to understand how land urbanization affected carbon emissions in Wuhan, Changsha, and Nanchang, the three major cities in the middle reaches of the Yangtze River, from 2000 to 2017. Following the core idea of th

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Xiao, Sun Xi, and Lin Wu. "Carbon Emissions Measurement of Jiangsu Province Industrial Energy Consumption Based on LMDI Method." Advanced Materials Research 1010-1012 (August 2014): 1932–36. http://dx.doi.org/10.4028/www.scientific.net/amr.1010-1012.1932.

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Energy consumption is the major source of industrial carbon emissions. Energy consumption carbon emission factor method and LMDI (Logarithmic Mean Divisia Index) method was used to analyze the carbon emission evolution of industrial economy energy consumption in Jiangsu Province with collected data on industrial energy consumption in 1995-2012. Results showed that Jiangsu province economic industrial carbon emissions keep increasing in 1995-2012 years. The results of carbon emission increase analysis of energy consumption structure effects, industrial energy consumption intensity effects and o

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Song, Limei, Jiang Chang, and Jianmei Yi. "A Bottom-Up Carbon Emission Assessment Model for Carbon Emission Control at the Level of Rural Detailed Planning." Land 13, no. 7 (2024): 1023. http://dx.doi.org/10.3390/land13071023.

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Incorporating green and low-carbon building targets into the rural planning management system requires scientific and quantitative methods for assessing carbon emissions from rural land use. At present, the research in this field mainly focuses on urban areas, but there are fewer theoretical and practical studies on the assessment of carbon emissions from rural land use. This paper proposes a new carbon emission assessment method based on land use modes, and the model can not only assess carbon emissions but also directly reflect the carbon emission intensity of different land use spaces in ru

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Sidorczuk-Pietraszko, Edyta. "Spatial Differences in Carbon Intensity in Polish Households." Energies 13, no. 12 (2020): 3108. http://dx.doi.org/10.3390/en13123108.

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Knowledge about the driving forces behind greenhouse gasses (GHG) emissions is crucial for informed and evidence-based policy towards mitigation of GHG emission and changing production and consumption patterns. Both national and regional-level authorities are capable of addressing their actions more effectively if they have information about the spatial distribution of phenomena related to the policies they conduct. In this context, the main aim of this paper is to explain the regional differences in carbon intensity in Poland. The differences in carbon intensity between regions and the nation

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Ma, Lei, and Mei Song. "Approaches to Carbon Emission Reductions and Technology in China’s Chemical Industry to Achieve Carbon Neutralization." Energies 15, no. 15 (2022): 5401. http://dx.doi.org/10.3390/en15155401.

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Based on China’s goal of achieving carbon neutrality by 2060, this study focused on its coal gasification in 2010–2019. Carbon emissions were calculated from industrial data, and an LMDt model was established to analyze the influencing factors of carbon emissions. Through scenario analysis, the paths of carbon emission reductions in the chemical industry were analyzed, and their emission reduction potential was estimated. The results showed that the carbon emissions in the chemical industry increased rapidly in 2010–2019, reaching 196 million tons in 2019. The emission structure was the most i

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Mao, Wenqing. "Analysis of influencing factors of carbon emissions in China based on the STIRPAT model." Theoretical and Natural Science 25, no. 1 (2023): 43–50. http://dx.doi.org/10.54254/2753-8818/25/20240898.

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China, as a major economic power, has been increasing its carbon emissions year after year. Effectively controlling carbon emissions and finding suitable and effective methods to reduce emissions have become the main research themes of current research. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model is used in this work to analyze the impact of GDP, population, urbanization, and energy intensity on Chinas carbon emissions from 2003 to 2020. From the output by the SPSS software, it can be illustrated that GDP and energy intensity have more obvious

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Lin, Qiaowen, Lu Zhang, Bingkui Qiu, Yi Zhao, and Chao Wei. "Spatiotemporal Analysis of Land Use Patterns on Carbon Emissions in China." Land 10, no. 2 (2021): 141. http://dx.doi.org/10.3390/land10020141.

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Nowadays, China is the world’s second largest economy and largest carbon emitter. This paper calculates the carbon emission intensity and the carbon emissions per capita of land use in 30 provinces at the national level in China from 2006 to 2016. A spatial correlation model is used to explore its spatiotemporal features. The results show that (1) China’s land use carbon emissions continued to grow from 2006 to 2016. The spatial heterogeneity of carbon emission intensity of land use initially decreased and then increased during this period. The carbon emission of land use pattern reached a pea

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Fan, Jia Feng, Hao Xu, Juan Yuan, and Bang Zhu Zhu. "Cluster Analysis of Industrial Transfer Park Based on Carbon Emission Intensity." Applied Mechanics and Materials 291-294 (February 2013): 1550–55. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.1550.

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The carbon emission control of Industrial Transfer Park is analyzed from four aspects. Metrics of these four aspects are energy consumption per industrial value added of leading industry, carbon emissions intensity of buildings, carbon emissions intensity of transportation and carbon sinks. On this basis, 36 industrial transfer parks in Guangdong province are analyzed with the method of Hierarchical cluster in order to explore practical measures to reduce carbon emissions in the parks.

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Chi, Liheng. "Impact Of Digital Economy On Carbon Emission Intensity." Frontiers in Business, Economics and Management 14, no. 1 (2024): 34–43. http://dx.doi.org/10.54097/7msyxw95.

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The rapid and widespread development of digital economy has given renewed impetus to efforts to peak and reduce carbon emissions, and has heralded significant changes in the energy sector. In this paper, panel data of 70 major countries from 2010 to 2019 are selected as samples, and an indicator system is constructed to measure the level of digital economy development in each country using the entropy weight method to study the development status of digital economy development and carbon emission intensity in each country. This research utilizes a regression model incorporating curve moderatin

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Chen, Xiaolan, Qinggang Meng, Jianing Shi, Yufei Liu, Jing Sun, and Wanfang Shen. "Regional Differences and Convergence of Carbon Emissions Intensity in Cities along the Yellow River Basin in China." Land 11, no. 7 (2022): 1042. http://dx.doi.org/10.3390/land11071042.

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Since the ecological protection and high-quality development of the Yellow River Basin (YRB) in China have become a primary national strategy, the low-carbon economy is crucial. To formulate effective emission mitigation policies for the YRB, we need to comprehensively understand the characteristics of the spatial agglomeration of the carbon emissions intensity in the YRB and its regional heterogeneity. Therefore, based on the relevant data from 2005 to 2017, we first scientifically measure the carbon emissions intensity of 57 cities along the YRB. Then, we analyze the spatial agglomeration ch

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Jing, Yuan Shu, Xin Long Wen, and Di Zhang. "International Comparison of Carbon Dioxide Emissions From Fuel Combustion of BRICS." Applied Mechanics and Materials 209-211 (October 2012): 1607–10. http://dx.doi.org/10.4028/www.scientific.net/amm.209-211.1607.

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Based on the latest national carbon dioxide emissions data released from the International Energy Agency (IEA), the carbon dioxide emissions trends of BRICS were analyzed in three aspects: the total carbon dioxide emissions, the emission intensity calculated using purchasing power parties (PPP) and per capita carbon dioxide emissions. The results show that the total carbon dioxide emissions among BRICS presented an increasing trend in different extent. On the other hand, the emission intensity calculated using PPP of BRICS showed a decreasing trend. The per capita carbon dioxide emissions of B

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