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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 (February 12, 2020): 798. http://dx.doi.org/10.3390/en13040798.
Повний текст джерелаАнотація:
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 of changes of CO2 emissions of individual industries) increased the amount of CO2 emissions and the CO2 emission intensity. The unfavourable CO2 emission intensity change came from the increased energy intensity of individual industries. The increased use of coal and electricity raised the CO2 emissions, whereas the insignificant change in emission factors showed little impact. Therefore, the study calls for policies that decrease the energy intensity of each industry by limiting the use of coal and reducing the electricity used by the manufacturing sector so that Thailand can make a positive contribution to the international community’s effort to achieve the goal of CO2 emissions reduction.
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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 (October 23, 2022): 13786. http://dx.doi.org/10.3390/ijerph192113786.
Повний текст джерелаАнотація:
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 considerably dominant (78%), followed by indirect (10%), transportation (8%), and household (2%) emissions on the national scale; (2) combining total emissions with emission intensity, high emission–high intensity cities, which are the most noteworthy regions, were concentrated in the North, while low emission–low intensity types mainly occurred in the South-West; (3) cities with a higher GDP tend to emit more CO2, while higher-income cities tend to emit less CO2, especially from the household sector. Cities with a developed economy, as indicated by GDP and income, would have low emissions per GDP, representing a high emission efficiency. Reducing the proportion of the secondary sector of the economy could significantly decrease CO2 emissions, especially for industrial cities. Therefore, the carbon reduction policy in China should focus on the industrial cities in the North with high emission–high intensity performance. Increasing the income and proportion of the tertiary industry and encouraging compact cities can effectively reduce the total emissions during the economic development and urbanization process.
3
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 (April 30, 2021): e0250995. http://dx.doi.org/10.1371/journal.pone.0250995.
Повний текст джерелаАнотація:
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 regression model for 14 countries, which accounted for more than 65% of food production in the world between 2000 and 2014. In this article, emission intensity is defined as the amount of GHG emissions per value of global output. Research on the determinants of GHG emissions related to food production is well documented in the literature; however, there is a lack of research on the determinants of the emission intensity ratio for food production. Hence, the original contribution of this paper is the analysis of the determinants of GHG emissions intensity of food production systems. The study found the decreased of emission intensity from an average of more than 0.68 kg of CO2 equivalent per USD 1 worth of food production global output in 2000 to less than 0.46 in 2014. The determinants of emission intensity decrease included the yield of cereals, the use of nitrogen fertilizers, the agriculture material intensity, the Human Development Index, and the share of fossil fuel energy consumption in total energy use. The determinants of growth of emission intensity of food production systems included GDP per capita, population density, nitrogen fertilizer production, utilized agriculture area, share of animal production, and energy use per capita.
4
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 (June 17, 2022): 7442. http://dx.doi.org/10.3390/su14127442.
Повний текст джерелаАнотація:
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 measures the regional emission reduction potential according to the environmental learning curve. The results showed that: (1) China’s overall carbon emissions continued to increase, but the carbon emission intensity showed an overall decreasing trend. There are strong regional differences in China’s carbon emission intensity. The carbon emission intensity in the western region is higher, and the overall regional difference is decreasing year by year. (2) China’s carbon emissions trading mechanism has a significant reduction effect, but the total quota slack of the Tianjin, Beijing, and Chongqing carbon emissions trading pilot markets is loose. (3) Shanghai, Shanxi, Jiangxi, Guizhou, Inner Mongolia, and Beijing are high-efficiency carbon emission reduction provinces (more than 35%), and Fujian and Xinjiang are low-efficiency carbon emission reduction provinces (less than 15%). It is necessary to further develop the demonstration effect of high emission reduction potential areas and increase the emission reduction efforts in low emission reduction potential areas.
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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 (June 19, 2022): 4464. http://dx.doi.org/10.3390/en15124464.
Повний текст джерелаАнотація:
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 economic growth and to promote the sustainable development of agriculture. The agricultural production efficiency is the main factor affecting the intensity of agricultural carbon emissions. Based on provincial panel data of China from 2010 to 2019, this paper establishes an indicator system and uses the super-efficiency SBM model to measure agricultural production efficiency. The regional agricultural carbon emissions were estimated using carbon-emission-related agricultural production activities. In order to study the nonlinear relationship between agricultural production efficiency and agricultural carbon emission intensity in the narrow sense, this paper uses a threshold regression model with agricultural carbon emissions as the threshold variable. Based on the analysis of China’s agricultural production efficiency and agricultural carbon emissions from 2010 to 2019, an empirical test is conducted through a threshold regression model. The results show an “inverted U-shaped” relationship between agricultural production efficiency and agricultural carbon emission intensity. In areas with high agricultural production efficiency, the improvement of production efficiency can suppress the intensity of agricultural carbon emissions; in areas with low agricultural production efficiency, the improvement of production efficiency increases the intensity of agricultural carbon emissions. Finally, based on the research conclusions, this paper provides feasible suggestions and countermeasures for China’s agricultural carbon emission reduction and improvement of agricultural production efficiency.
6
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 (January 6, 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 findings are as follows. (1) The carbon emission intensity has time-dependence and positive spatial spillover effect. That is, there is a close correlation between current and early carbon emissions, and there is also a high-degree correlation between regional and surrounding areas’ carbon emissions. (2) Carbon emissions keep a classical inverted U-shaped relation with innovation agglomeration, as well as with energy intensity. However, the impact of innovation agglomeration on carbon emissions in inland regions of China does not appear on the right side of the inverted U-shaped curve, while carbon emissions are subject to a positive nonlinear promoting effect from energy intensity. (3) When the logarithm of innovation agglomeration is more than 3.0309, it first shows the inhibition effect on energy intensity. With the logarithm of innovation agglomeration exceeding 5.0100, it will show the dual effect of emission reduction and energy conservation. (4) Energy intensity could work as the intermediary variable of innovation agglomeration’s influence on carbon emissions. Through its various positive externalities, innovation agglomeration can produce a direct impact on carbon emissions, and through energy intensity, it can also affect carbon emissions indirectly.
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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 (September 29, 2022): 12441. http://dx.doi.org/10.3390/ijerph191912441.
Повний текст джерелаАнотація:
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 growth rate of carbon emission quantity and carbon emission intensity of production energy consumption decreased year by year in each province during the study period. The imbalance of carbon emission was strong, with different degrees of increase and decrease, and there were big differences between eastern and western regions. The classification of carbon emissions differed among provinces and there was heterogeneity among regions. The quantity and intensity of carbon emissions of production energy consumption qwre affected by multiple factors, such as industrial structure. This study provides an in-depth comparison of the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions of production energy consumption across the country and regions, and provides targeted policies for carbon emission reduction across the country and regions, so as to help achieve China’s “double carbon” target quickly and effectively.
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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 (November 5, 2018): 2467. http://dx.doi.org/10.3390/ijerph15112467.
Повний текст джерелаАнотація:
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 factors on carbon emissions is analyzed quantitatively. Results show that the R&D intensity and energy intensity are the main factors inhibiting the growth of carbon emissions. GDP and investment intensity are the major factors promoting the growth of carbon emissions. The contribution of R&D efficiency to carbon emissions is decreasing. The impacts of energy structure and province structure on carbon emissions are ambiguous through time. Finally, some policy suggestions for strengthening the management of carbon emissions and carbon emission reduction are proposed.
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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 (June 24, 2014): 65. http://dx.doi.org/10.5539/sar.v3n3p65.
Повний текст джерелаАнотація:
<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 Canada. The protein based GHG emission intensity is 60% to 90% higher for sheep than for beef cattle in Canada. The GHG emission intensity for sheep in Eastern Canada is higher than for sheep in Western Canada. Protein based GHG emission intensity is more sensitive to the difference between sheep and beef than LW based emission intensity. This paper demonstrated that protein based GHG emission intensity is a more meaningful indicator for comparing different livestock species than live weight (LW) based GHG emission intensity.</p>
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Qin, Jiancheng, Hui Tao, Chinhsien Cheng, Karthikeyan Brindha, Minjin Zhan, Jianli Ding, and Guijin Mu. "Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China." Sustainability 12, no. 3 (February 4, 2020): 1089. http://dx.doi.org/10.3390/su12031089.
Повний текст джерелаАнотація:
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 stages. During 1990–2000, economic development and population were the main factors contributing to the increase in carbon emissions, and energy intensity was an important factor to curb the carbon emissions increase. The energy intensity of industry and the economic structure of agriculture were the main factors to promote the decline of entire energy intensity. During 2001–2010, economic growth and emission coefficient were the main drivers to escalate the carbon emissions, and energy intensity was the key factor to offset the carbon emissions growth. The economic structure of transportation, and the energy intensity of industry and service were the main factors contributing to the decline of the entire energy intensity. During 2011–2016, economic growth and energy intensity were the main drivers of enhancing carbon emissions, while the coefficient was the key factor in curbing the growth of carbon emissions. The industry’s economic structure and transportation’s energy intensity were the main factors to promote the decline of the entire energy intensity. Finally, the suggestions of emissions reductions are put forward from the aspects of improving energy efficiency, optimizing energy structure and adjusting industrial structure etc.
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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 (August 31, 2022): 426–31. http://dx.doi.org/10.35530/it.073.04.202149.
Повний текст джерелаАнотація:
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 spillover has increased them. FDI technology spillover has increased CO2 emission intensity, but its impact on CO2 emissions isn’t significant. Therefore, China should take domestic R&D investment as the key measure to reduce CTI’s CO2 emissions in the future and continue to improve the level of independent innovation. China should also attract more low-carbon and green international investment and avoid becoming the "pollution heaven" for high-emission capital. The level of technology embedded in the imported textile products should be improved further. The use of various technological innovation strategies not only reduces CTI’s CO2 emissions but also makes positive contributions to China's goal of "carbon peaking and carbon neutralization".
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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 (November 1, 2021): 14–26. http://dx.doi.org/10.37934/arfmts.88.2.1426.
Повний текст джерелаАнотація:
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 Climate Change (IPCC) methodologies was adopted. Based on this study, the highest GHG emission intensity were from coal power plants which ranged from 0.67 – 0.85 tCO2/ MWh. The GHG emission intensity for natural gas power plants ranged from 0.38 – 0.78 tCO2/ MWh. The overall GHG emission intensity for all power plants studied was estimated to be 0.54 tCO2/ MWh. The large variations in CO2 emissions per MWh of electricity generated in fossil fuel power plants were due to differences in generation efficiency, fuel selection, technology, and plant age. In supporting Malaysia’s conditional commitment of 45% GHG emissions intensity reduction target against the country’s GDP, the emission reduction strategies up to 2025 were assessed using three key scenarios namely Business-As-Usual (BAU), Planning (PLAN) and Ambitious (AMB). Based on the analysis, the projection indicates that the emissions intensity for the power sector is about 0.79 tCO2/ MWh, 0.49 tCO2/ MWh, and 0.44 tCO2/ MWh under the BAU, PLN AMB scenarios respectively. Finally, GHG emission reduction potentials were also outlined in this paper.
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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.
Повний текст джерелаАнотація:
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 influencing factors are economy and industrial structure, as well as reducing energy consumption intensity has also contributed more to the suppression of carbon emissions. Population and carbon emission intensity vary slightly by region. Therefore, governments should guide reasonable population growth, formulate reasonable carbon emission reduction policies according to local conditions, optimize the energy structure and accelerate the establishment of carbon markets.
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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 (January 4, 2023): 921. http://dx.doi.org/10.3390/ijerph20020921.
Повний текст джерелаАнотація:
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 leading to decoupling. The obtained results show that: (1) Innovation input scale and GDP are the main drivers for increases in PM2.5 emissions, while innovation input PM2.5 intensity, emission intensity, and emission coefficient are the main reasons for reductions in PM2.5 pollution. (2) China and its four subregions show general upward trends in the decoupling index, and their decoupling states turn from weak decoupling to strong decoupling. (3) Innovation input PM2.5 intensity, emission intensity, and emission coefficient contribute largely to the decoupling of PM2.5 emissions. Overall, this paper provides valuable information for mitigating haze pollution.
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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 plants are obtained.
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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 (February 17, 2022): 2321. http://dx.doi.org/10.3390/ijerph19042321.
Повний текст джерелаАнотація:
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 and passenger transport intensity are critical for reducing emissions from road passenger transport, and transport structure change is causing a shift in emissions between different passenger transport modes; and (2) the most effective strategies to reduce carbon emissions in the road freight transport sector are to improve energy intensity and reduce freight transport intensity. Several policy recommendations regarding reducing energy and transport intensity are proposed. The results and policy recommendations are expected to provide useful references for policy makers to form carbon emissions reduction strategies for the road transport sector.
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Sidorczuk-Pietraszko, Edyta. "Spatial Differences in Carbon Intensity in Polish Households." Energies 13, no. 12 (June 16, 2020): 3108. http://dx.doi.org/10.3390/en13123108.
Повний текст джерелаАнотація:
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 national average were analysed using index decomposition analysis (IDA). Aggregate carbon intensity for regional economies as well as the carbon intensity of households was investigated. For both levels of analysis: total emissions and emission from households economic development is the key factor responsible for the inter-regional differences in carbon emission per capita. In the case of total emissions, the second important factor influencing these differences is the structure of the national power system, i.e., its concentration and the production of energy from fossil fuels. For households, disposable income per capita is a key factor of differences in CO2 emission per capita between regions. Higher households’ incomes contribute to higher emission per capita, mostly due to the shift in consumption towards more energy- and material-intensive goods. The contribution of energy emissivity is quite low and not as varied as in the case of income. This suggests that policy instruments targeted at the consumption of fuels can be rather uniform across regions, while more developed regions should also be subject to measures supporting less energy-intensive consumption. On the other hand, policy in less developed regions should prevent them from following the path of per capita emissions growth.
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Shrestha, Prativa, and Changyou Sun. "Carbon Emission Flow and Transfer through International Trade of Forest Products." Forest Science 65, no. 4 (May 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 all countries have become cleaner based on the emission intensity value. Carbon emissions embodied in international trade of forest products are about 25 percent of total emissions from production activities. Developing countries generally have much higher emission intensities than developed countries. Uncertainties in the carbon emission data have a larger impact than those in the intermediate and final consumption data. These findings are helpful for policymakers to understand the economic–environmental relations of forest products trade and to improve policy and agreement designs.
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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 (May 4, 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 emissions far below the previously reported intensities. This happened when the green line intensity exceeds the red line intensity. In this work we discuss the behaviour of electron precipitation characteristics and optical emissions during NEIAL events on the nightside, and we suggest that intensity enhancements in the 844.6 nm emission line could be a better candidate than the 630.0 nm emission as an optical signature for NEIALs.
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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 (December 16, 2019): 4800. http://dx.doi.org/10.3390/en12244800.
Повний текст джерелаАнотація:
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 investment (FDI) and energy intensity on China’s manufacturing carbon emissions through the fixed-effect panel quantile regression model. The results show that the effects of economic growth, FDI and energy intensity on carbon emissions of the manufacturing industry are different in different levels and regions, and they have apparent heterogeneity. In particular, economic growth plays a decisive role in the CO2 emissions of the manufacturing industry. Economic growth has a positive impact on the carbon emissions of the manufacturing industry; specifically, a higher impact on high carbon emission provinces. Besides, FDI has a significant positive effect on the upper emission provinces of the manufacturing industry, which proves that there is a pollution paradise hypothesis in China’s manufacturing industry, but no halo effect hypothesis. The reduction of energy intensity does not have a positive effect on the reduction of carbon emissions. The higher impact of the energy intensity of upper emission provinces on carbon emissions from their manufacturing industry, shows that there is an energy rebound effect in China’s manufacturing industry. Finally, our study confirms that China’s manufacturing industry has considerable space for emission reduction. The results also provide policy recommendations for policymakers.
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Oh, Wankeun, and Jonghyun Yoo. "Long-Term Increases and Recent Slowdowns of CO2 Emissions in Korea." Sustainability 12, no. 17 (August 26, 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 growth and population growth. However, improvements in energy intensity, carbon intensity, and economic structure offset large portions of CO2 emissions. The recent slowdown (2012–2015) mainly resulted from a decline in energy intensity and carbon intensity in the economic sectors. Among the different energy types, electricity has played a significant role in decreasing emissions because industries have reduced the consumption of electricity per output and the source of electricity generation has shifted to cleaner energies. These results imply that the Korean government should support strategies that reduce energy intensity and carbon intensity in the future to reduce CO2 emissions and maintain sustainable development.
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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 industry is reliant on estimating emissions using tools such as the Australian Dairy Carbon Calculator (ADCC). The present study compared the emission profiles of 41 Australian dairy farms with ADCC using the old (pre-2015) and new (post-2015) NGGI methodologies to examine the impact of the changes on the emission intensity across a range of dairy-farm systems. The estimated mean (±s.d.) GHG emission intensity increased by 3.0%, to 1.07 (±0.02) kg of carbon dioxide equivalents per kilogram of fat-and-protein-corrected milk (kg CO2e/kg FPCM). When comparing the emission intensity between the old and new NGGI methodologies at a regional level, the change in emission intensity varied between a 4.6% decrease and 10.4% increase, depending on the region. When comparing the source of emissions between old and new NGGI methodologies across the whole dataset, methane emissions from enteric fermentation and waste management both increased, while nitrous oxide emissions from waste management and nitrogen fertiliser management, CO2 emissions from energy consumption and pre-farm gate (supplementary feed and fertilisers) emissions all declined. Enteric methane remains a high source of emissions and so will remain a focus for mitigation research. However, these changes to the NGGI methodology have highlighted a new ‘hotspot’ in methane from manure management. Researchers and farm managers will have greater need to identify and implement practices on-farm to reduce methane losses to the environment.
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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 decreased, indicating that the energy efficiency was improved continuously. (2) Transport carbon emissions were in a growing trend. The greatest influence factor was the economic growth which had a positive effect and enlarged transportation carbon emissions quantity. On the other hand, the factors of the transportation energy intensity had a negative effect. Except 2011, the transportation energy structure always had a negative effect, which reduced transportation carbon emissions quantity.
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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 (March 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 emissions in China are mainly concentrated in North China, East China and Chongqing, while cities with the highest per capita CO2 emissions and emission intensity are mainly concentrated in Northwest and North China. (2) Different types of cities have different CO2 emission characteristics. Resource-based cities have a higher total amount and emission intensity; tourism and underdeveloped cities both have lower values; while super-large-sized cities and many very-large-sized cities have higher CO2 emissions, but their emission intensities are usually lower; and no obvious rules are found in other cities. (3) Spatial analysis shows that cities with higher CO2 emissions are clustered. The Beijing–Tianjin–Hebei region, the Yangtze River Delta region, Shandong Province, and Shanxi–Henan–Anhui resource-producing areas are the agglomeration areas of high-emission cities. (4) Scale analysis shows that the characteristics of CO2 emissions at different scales are different. Provincial-level research can help to identify the environmental impact and total effect of carbon emissions, while urban-scale research is helpful to explore the diversity and phases of cities. Finally, based on the main conclusions of this study, the corresponding urban low-carbon policy implications are drawn.
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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 resources that can be used to develop energy-efficient technologies. In the post-Soviet space and the Asia-Pacific region, a significant effect on reducing emission intensity is provided by environmental policy’s effectiveness to minimize carbon dioxide emissions.
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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 (April 18, 2018): 11–20. http://dx.doi.org/10.1177/0361198118755714.
Повний текст джерелаАнотація:
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 this paper, we propose a different approach to reducing transportation GHG emissions, by catering policies to specific trips based on their emission burden. We focus on the City of Toronto downtown. Using second-by-second speed data for entire trajectories, GHGs (in CO2eq) and nitrogen oxides (NOx) emissions were estimated. We observe that the destinations attracting the highest trip emissions tend to be in the hospital and financial districts. Trips originating and ending in the downtown area are responsible for a small share of total emissions, although they have high emission intensity. Removing trips with high total emissions and high emission intensity led to significant reductions in CO2eq and NOx emissions, whereas removing shorter trips, did not have a significant influence on total emissions nor emission intensities.
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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 output scale effects in 1999-2012 showed that energy consumption intensity effect has the maximum contribution to carbon emissions in industrial carbon emissions Jiangsu Province. Therefore, the main way to control carbon emissions of industrial energy consumption in Jiangsu Province is reasonably control the growth of energy consumption.
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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 (February 3, 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 the Kaya identity model, sources of carbon emissions are decomposed into eight factors: urban expansion, economic level, industrialization, population structure, land use, population density, energy intensity, and carbon emission intensity. Furthermore, using the Logarithmic Mean Divisia Index (LMDI), we analyze how the different time periods and time series driving forces, especially land urbanization, affect regional carbon emissions. The results indicate that the total area of construction land and the total carbon emissions increased from 2000 to 2017, whereas the growth in carbon emissions decreased later in the period. Energy intensity is the biggest factor in restraining carbon emissions, followed by population density. Urban expansion is more significant than economic growth in promoting carbon emissions, especially in Nanchang. In contrast, the carbon emission intensity has little influence on carbon emissions. Changes in population structure, industrial level, and land use vary regionally and temporally over the different time period.
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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 (July 26, 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 important factor in mitigating carbon emissions, and the emission intensity, industrial structure, economic development level, and labor force scale had different degrees of promotion effects, of which emission intensity was the strongest. The chemical industry can reach a carbon peak before 2030 under the three analyzed scenarios, and the emission reduction potential is the largest under the landing policy scenario. The results showed that carbon capture, usage, and storage (CCUS) technology is key for carbon emission reductions and that it is necessary to adjust the industrial structure, reduce emission intensity, and increase forest carbon sink to achieve carbon neutrality in the chemical industry.
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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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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 (February 1, 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 peak in 2015 and the land use carbon emission intensity was relatively lower in east China; (2) southern China accounts for a majority of the total Chinese carbon sink. Better economic structure, land use structure and industrial structure will lead to lower carbon emission intensity of land use; (3) carbon emissions per capita of land use in China are affected not only by land development intensity, urbanization level, and energy consumption structure, but also by the population policy. It is significant to formulate differentiated energy and land use policies according to local conditions. This study not only provides a scientific basis for formulating different carbon emission mitigation policies for the local governments in China, but also provides theoretical reference for other developing countries for sustainable development. It contributes to the better understanding of the land use patterns on carbon emissions in China.
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Hsu, Kun Jung. "The Changing of Taiwan Housing Energy Intensity and Emission Intensity." Applied Mechanics and Materials 174-177 (May 2012): 3556–59. http://dx.doi.org/10.4028/www.scientific.net/amm.174-177.3556.
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Combining the informations of Taiwan energy balance sheet and National Housing Survey, the paper explores the changing of the housing energy use intensity and emissions intensity. Results of the analysis showed that electricity is the dominant energy type used in Taiwan housing from 1986 to 2005; the accumulated logarithmic growth of electricity use intensity from 1986 to 2005 was 60.1%. The accumulated logarithmic growth rate of electricity emissions intensity from 1986 to 2005 was 96.9%. The major effect of Taiwan housing emissions intensity was electricity use intensity during the study period.
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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 BRICS also presented an increasing trend in different extent. The Russian Federation and South Africa’s per capita carbon dioxide emissions were higher than the World’s average level, whilst those of India, Brazil and China were lower than the World’s average level, which is far less than the level of the OECD countries.
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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 (July 8, 2022): 1042. http://dx.doi.org/10.3390/land11071042.
Повний текст джерелаАнотація:
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 characteristics and long-term transfer trends of carbon emission intensity using exploratory spatial data analysis methods and Markov chains. Finally, the Dagum Gini coefficient and the variation coefficient method are used to study the regional differences and differential evolution convergence of the carbon emissions intensity in the YRB. The results show that the carbon emissions intensity of the YRB has dropped significantly with the spatial distribution characteristics “high in the west and low in the east”, and there is a significant spatial autocorrelation phenomenon. In addition, the probability of a shift in urban carbon intensity is low, leading to a “club convergence” and a “Matthew effect” in general and across regions. Inter-regional differences have always been the primary source of spatial differences in carbon emissions intensity in the YRB, and the intra-regional differences in carbon emissions intensity in the lower YRB show a significant convergence phenomenon. The research results may provide a reference for the regional coordinated development of a low-carbon economy in the YRB, and serve to guide the win-win development model of ecological environment protection and economic growth in the YRB.
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Wu, Qiong, Kanittha Tambunlertchai, and Pongsa Pornchaiwiseskul. "Examining the Impact and Influencing Channels of Carbon Emission Trading Pilot Markets in China." Sustainability 13, no. 10 (May 18, 2021): 5664. http://dx.doi.org/10.3390/su13105664.
Повний текст джерелаАнотація:
As China has an important role in global climate change, the Chinese government has set goals to improve its environmental efficiency and performance and launched carbon emission trading pilot markets in 2013, aiming to reduce CO2 emissions. Based on panel data of 30 provinces from 2005 to 2017, this paper uses the difference-in-difference method to study the impact of China’s carbon emission trading pilot markets on carbon emissions and regional green development. The paper also explores possible influencing channels. The main conclusions are as follows: (1) China’s carbon emission trading policy has promoted a reduction in CO2 emissions and carbon emission intensity and has increased green development in the pilot areas. (2) The main path for China’s carbon emission trading policy to achieve carbon emission reduction and regional green development is to promote technology adoption. (3) China’s carbon emission trading policy achieves green development through synergistic SO2 emission reduction. The pilot carbon markets have reduced both the amount of SO2 emissions and SO2 emission intensity.
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Nie, Yongyou, Yunhuan Gao, and He He. "Modelling Structural Effect and Linkage on Carbon Emissions in China: An Environmentally Extended Semi-Closed Ghosh Input–Output Model." Energies 15, no. 17 (August 23, 2022): 6104. http://dx.doi.org/10.3390/en15176104.
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The carbon emissions of sectors and households enabled by primary inputs have practical significance in reality. Considering the mutual effect between the industrial sector and the household, this paper firstly constructed an environmentally extended semi-closed Ghosh input–output model with an endogenized household sector to analyze the relationship between carbon emissions and the Chinese economy from the supply-side perspective. The structural decomposition analysis and the hypothetical extraction method were remodified to identify the supply-side driving effects of the changes in carbon emissions and investigate the net carbon linkage. The results show that the electricity, gas, and water supply sector was the key sector with the highest carbon emission intensity enabled by primary inputs. The household sector had an above 93% indirect effect of the enabled intensity, with its enabled intensity dropping significantly by more than 55% from 2007 to 2017. The operating surplus and mixed income caused 3214.67 Gt (34.17%) of the enabled emissions in 2017. The supply-side economic activity, measured by the value added per capita, was the main factor of the carbon emission growth, mainly attributed to the development of the manufacturing sector and the electricity, gas, and water supply sector. The emission intensity and allocation structure both brought a decrease in carbon emissions. The electricity, gas, and water supply sector and the manufacturing sector were the major sources of the supply-induced cross-sectoral input emissions, while the commercial and service sector and the household sector were the top source of supply-induced cross-sectoral output emissions. This paper sheds light on the policies of the carbon emission abatement and the adjustment of the allocation structure from the perspective of supply.
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Huo, Congjia, and Lingming Chen. "The Impact of the Income Gap on Carbon Emissions: Evidence from China." Energies 15, no. 10 (May 20, 2022): 3771. http://dx.doi.org/10.3390/en15103771.
Повний текст джерелаАнотація:
The income gap and global warming have always been topics of common concern to scholars worldwide. Internationally, there is no consensus yet about the impact of the income gap on carbon emissions, and there are few studies about that in China. To explore the effect of the income gap on carbon emissions at the provincial level in China, this paper first theoretically and qualitatively analyzes the non-linear impact of the income gap on carbon emissions. Then, the Gini coefficient of the resident income of different regions in China from 2010 to 2019 is calculated. Finally, a threshold regression model is used to quantitatively test the existence of a threshold effect between the income gap and carbon emission intensity in China. The threshold value is the per capita disposable income of residents. The results show that the income gap is positively related to carbon emission intensity in poor regions. In high-income areas, the widening income gap inhibits the increase in carbon emission intensity. Based on this, this paper proposes policy recommendations to narrow the income gap and reduce the intensity of carbon emissions.
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Zhang, Caiqing, Mi Zhang, and Nan Zhang. "Identifying the Determinants of CO2 Emission Change in China’s Power Sector." Discrete Dynamics in Nature and Society 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2626418.
Повний текст джерелаАнотація:
Power sector is significantly important for China to achieve the CO2 emission reduction targets. In this study, we analyze the features of CO2 emissions and environment effect in China’s power sector, investigate the driving factors of CO2 emission change based on the logarithmic mean Divisia index (LMDI) method, and evaluate the mitigation potential of CO2 emissions in China’s power sector. Results show that CO2 emissions in China’s power sector increased rapidly from 492.00 Mt in 1990 to 3049.88 Mt in 2014 while CO2 emission intensity experienced an unsteady downward trend during the study period. Industrial scale effect is the key contributor to CO2 emission growth in China’s power sector, and its contribution degree reaches 123.97%. Energy intensity effect contributes most to the decrease in CO2 emissions, with a contribution degree of −20.01%. Capital productivity effect is another important factor leading to CO2 emissions increase. The aggregate CO2 emission reduction would reach 17973.86 million tons (Mt) during 2015–2030 in the ideal emission reduction scenario. Finally, policy recommendations are made for future energy-saving and CO2 emission reduction in China’s power sector.
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Josephson, Alexander J., Daniel Castaño, Marlin J. Holmes, and Rodman R. Linn. "Simulation Comparisons of Particulate Emissions from Fires under Marginal and Critical Conditions." Atmosphere 10, no. 11 (November 13, 2019): 704. http://dx.doi.org/10.3390/atmos10110704.
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Using a particulate emissions model developed for FIRETEC, we explore differences in particle emission profiles between high-intensity fires under critical conditions and low-intensity fires under marginal conditions. Simulations were performed in a chaparral shrubland and a coniferous pine forest representative of the southeast United States. In each case, simulations were carried out under marginal and critical fire conditions. Marginal fire conditions include high moisture levels and low winds, often desired for prescribed fires as these conditions produce a low-intensity burn with slower spread rates. Critical fire conditions include low moisture levels and high winds, which easily lead to uncontrollable wildfires which produce a high-intensity burn with faster spread rates. These simulations’ resultant particle emission profiles show critical fire conditions generate larger particle emission factors, higher total mass emissions, and a higher lofting potential of particles into the atmosphere when compared against marginal fire conditions but similar particle size distrubtions. In addition, a sensitivity analysis of the emissions model was performed to evaluate key parameters which govern particle emission factor and particle size.
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Tseng, Sheng-Wen. "Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China." Sustainability 11, no. 24 (December 8, 2019): 7008. http://dx.doi.org/10.3390/su11247008.
Повний текст джерелаАнотація:
Inner Mongolia has shown both rapid economic growth and a large renewable energy base, this has come about by the introduction of the “Western Development” strategy and renewable energy policy of the Chinese Government. However, this has led to a contradictory situation where both high carbon emission and reduction exist together. The average economic growth of Inner Mongolia reached 15.76% between 2006 and 2016, which caused huge CO2 emissions. However, promotion of the renewable energy policy (since 2005) resulted in an energy self-sufficiency rate that reached 270.80% by 2016. In this study of the Inner Mongolia carbon emission situation, the logarithmic mean divisia index (LMDI) model was used to analyze the factors affecting carbon emission fluctuations from 2005 to 2016. The decoupling elasticity index was then used to measure the decoupling effect of the economic growth and carbon emissions. The results of this research show that: firstly, CO2 emissions increased rapidly from 651.03 million tons in 2006 to 1723.24 million tons in 2013. Despite a slight decline in CO2 emissions, a level above 1600 million tons was maintained between 2014 and 2016. Secondly, the industry sector was the main source of CO2 emissions in Inner Mongolia, and coal-based fuel played a determining role. Thirdly, in this study, two important contributions were made, including the discovery of two new drivers: labor and emission intensity factors. Further, findings about the effect of the six industrial sectors, economic structure, energy density, and emission intensity factors were also decomposed. It was found that during research period, the population factor, labor factor, and labor productivity factor all had a positive influence on CO2 emissions, whereas the economic structure factor and emission intensity factor had different impacts on the CO2 emissions depending on the particular industrial sector. Furthermore, the energy intensity of six industrial sectors contributed to the decrease in aggregate CO2 emissions. Finally, in this study, it was also found that economic growth and CO2 growth in Inner Mongolia presented a weak decoupling state. Policy recommendations based on these results have been presented.
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Qin, Chang Cai, Shu Lin Liu, and Yu Feng Wang. "A Comparative Study about Carbon Emissions." Advanced Materials Research 518-523 (May 2012): 1657–63. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.1657.
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This article has introduced and evaluated the various methods of study on carbon emissions, and makes a comparison on the research conclusion by using these methods. We has classified the influence factors of carbon emissions into three primary factors such as technical factor, structure factor and scale factor, respectively including six secondary factors such as carbon emission intensity and energy intensity; energy structure and industrial structure; economic scale, population size.
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Sadorsky, Perry. "Energy Related CO2 Emissions before and after the Financial Crisis." Sustainability 12, no. 9 (May 9, 2020): 3867. http://dx.doi.org/10.3390/su12093867.
Повний текст джерелаАнотація:
The 2008–2009 financial crisis, often referred to as the Great Recession, presented one of the greatest challenges to economies since the Great Depression of the 1930s. Before the financial crisis, and in response to the Kyoto Protocol, many countries were making great strides in increasing energy efficiency, reducing carbon dioxide (CO2) emission intensity and reducing their emissions of CO2. During the financial crisis, CO2 emissions declined in response to a decrease in economic activity. The focus of this research is to study how energy related CO2 emissions and their driving factors after the financial crisis compare to the period before the financial crisis. The logarithmic mean Divisia index (LMDI) method is used to decompose changes in country level CO2 emissions into contributing factors representing carbon intensity, energy intensity, economic activity, and population. The analysis is conducted for a group of 19 major countries (G19) which form the core of the G20. For the G19, as a group, the increase in CO2 emissions post-financial crisis was less than the increase in CO2 emissions pre-financial crisis. China is the only BRICS (Brazil, Russia, India, China, South Africa) country to record changes in CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period that were lower than their respective values in the pre-financial crisis period. Compared to the pre-financial crisis period, Germany, France, and Italy also recorded lower CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period. Germany and Great Britain are the only two countries to record negative changes in CO2 emissions over both periods. Continued improvements in reducing CO2 emissions, carbon intensity and energy intensity are hard to come by, as only four out of nineteen countries were able to achieve this. Most countries are experiencing weak decoupling between CO2 emissions and GDP. Germany and France are the two countries that stand out as leaders among the G19.
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Li, Wei, Hao Li, Huixia Zhang, and Shuang Sun. "The Analysis of CO2Emissions and Reduction Potential in China’s Transport Sector." Mathematical Problems in Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1043717.
Повний текст джерелаАнотація:
China’s transport sector is responsible for approximately 10% of national CO2emissions. In the process of industrialization and urbanization of China, emissions from transport sector would continuously increase. In order to investigate the emissions and reduction potential and provide the policy guidance for policymakers in China’s transport sector, this study decomposed the CO2emissions using the Kaya identity, calculated the contribution based on the Logarithmic Mean Divisia Index (LMDI) method to explore the underlying determinants of emissions change, and then constructed different scenarios to predict the emissions and estimate the potential of emission reduction in the future. Results indicated that carbon emissions in China’s transport sector have increased from 123.14 Mt in 1995 to 670.76 Mt in 2012. Income effect is the dominant factor that results in the increase of emissions while energy intensity effect is the main driving force to lower carbon emissions. The transportation modal shifting, transportation intensity change, and population growth have the positive but relatively minor impact on emissions. The accumulated emission reduction is expected to be 1825.97 Mt, which is 3 times more than the emissions in 2010. Policy recommendations are thus put forward for future emission reduction.
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Li, Yongqing, Ian Eddie, and Jinghui Liu. "Carbon emissions and the cost of capital: Australian evidence." Review of Accounting and Finance 13, no. 4 (November 4, 2014): 400–420. http://dx.doi.org/10.1108/raf-08-2012-0074.
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Purpose – The purpose of this paper is to investigate the potential impact of the approved Australian carbon emissions reduction plan on the cost of capital and the association between companies’ carbon emission intensity and the cost of capital. Design/methodology/approach – A sample of Australian Stock Exchange 200 (ASX 200)-indexed companies from 2006 to 2010 is used. Hypotheses are tested based on Heckman’s two-stage approach. Three regression models are developed to examine the association between carbon emissions and the cost of capital. Findings – Using a sample of ASX 200-indexed listed companies, the paper finds that the cost of capital, including the cost of debt and the cost of equity, will increase for emissions-liable companies. Results also show that the cost of debt is positively correlated with a company’s emission intensity. However, little evidence supports that the emission intensity affects the cost of equity. Originality/value – As it is evident that the emissions reduction plan will adversely affect corporate entities’ cost of capital, this study suggests that companies, investors and lenders need to include carbon emission in risk analysis. An emissions-liable company should establish strategies to combat the impact of the Plan on rising cost that comes with the enforcement of the Plan. Government assistance is essential in the transitional period.
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Allan, Travis, and Kathy Baylis. "Sinks, Emissions Intensity Caps and Barriers to Emissions Trading." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 53, no. 4 (December 2005): 291–305. http://dx.doi.org/10.1111/j.1744-7976.2005.00020.x.
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Pang, Jiaxing, Hengji Li, Chengpeng Lu, Chenyu Lu, and Xingpeng Chen. "Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China." International Journal of Environmental Research and Public Health 17, no. 20 (October 16, 2020): 7541. http://dx.doi.org/10.3390/ijerph17207541.
Повний текст джерелаАнотація:
The study of the carbon emission intensity of agricultural production is of great significance for the formulation of a rational agricultural carbon reduction policy. This paper examines the regional differences, spatial–temporal pattern and dynamic evolution of the carbon emission intensity of agriculture production from 1991 to 2018 through the Theil index and spatial data analysis. The results are shown as follows: The overall differences in carbon emission intensity of agriculture production presents a slightly enlarging trend, while the inter-regional differences in carbon emissions intensity is decreasing, but the intra-regional difference of carbon emissions intensity presented an expanding trend. The difference in carbon emission intensity between the eastern and central regions is not obvious, and the difference in carbon emission intensity in the western region shows a fluctuating and increasing trend. The overall differences caused by intra-regional differences; the average annual contribution of intra-regional differences is 67.84%, of which the average annual contribution of western region differences is 64.24%. The carbon emission intensity of agricultural production in China shows a downward trend, with provinces with high carbon emission intensity remaining stable, while provinces with low intensity are expanding. The Global Moran’s I index indicates that China’s carbon emission intensity of agricultural production shows a clear trend of spatial aggregation. The agglomeration trend of high agricultural carbon emission remains stable, and the overall pattern of agricultural carbon emission intensity shows a pattern of increasing differentiation from east to west.
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Li, Wei, and Qing-Xiang Ou. "Decomposition of China’s Carbon Emissions Intensity from 1995 to 2010: An Extended Kaya Identity." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/973074.
Повний текст джерелаАнотація:
This paper employs an extended Kaya identity as the scheme and utilizes the Logarithmic Mean Divisia Index (LMDI II) as the decomposition technique based on analyzing CO2emissions trends in China. Change in CO2emissions intensity is decomposed from 1995 to 2010 and includes measures of the effect of Industrial structure, energy intensity, energy structure, and carbon emission factors. Results illustrate that changes in energy intensity act to decrease carbon emissions intensity significantly and changes in industrial structure and energy structure do not act to reduce carbon emissions intensity effectively. Policy will need to significantly optimize energy structure and adjust industrial structure if China’s emission reduction targets in 2020 are to be reached. This requires a change in China’s economic development path and energy consumption path for optimal outcomes.
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Li, Sen, Yanwen Lan, and Lijun Guo. "Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China." Nature Environment and Pollution Technology 21, no. 2 (June 1, 2022): 447–56. http://dx.doi.org/10.46488/nept.2022.v21i02.003.
Повний текст джерелаАнотація:
Estimating carbon emissions and assessing their contribution are critical steps toward China’s objective of reaching a “carbon peak” in 2030 and “carbon neutrality” in 2060. This paper selects relevant statistical data on carbon emissions from 2000 to 2018, combines the emission coefficient method and the Logarithmic Mean Divisia Index model (LMDI) to calculate carbon emissions, and analyses the driving force of carbon emission growth using Henan Province as a case study. Based on the partial least squares regression analysis model (PLS), the contributions of inter-provincial factors of carbon emission are analyzed. Finally, a county-level downscaling estimation model of carbon emission is further formulated to analyze the temporal and spatial distribution of carbon emissions and their evolution. The research results show that: 1) The effect of energy intensity is responsible for 82 percent of the increase in carbon emissions, whereas the effect of industrial structure is responsible for -8 percent of the increase in carbon emissions. 2) The proportion of secondary industry and energy intensity, which are 1.64 and 0.82, respectively, have the most evident explanatory effect on total carbon emissions; 3). Carbon emissions vary widely among counties, with high emissions in the central and northern regions and low emissions in the southern. However, their carbon emissions have constantly decreased over time. 4) The number of high-emission counties, their carbon emissions, and the degree of their discrepancies are gradually reduced. The findings serve as a foundation for relevant agencies to gain a macro-level understanding of the industrial landscape and to investigate the feasibility of carbon emission reduction programs.
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Liu, Lei, Ke Wang, Shanshan Wang, Ruiqin Zhang, and Xiaoyan Tang. "Exploring the Driving Forces and Reduction Potential of Industrial Energy-Related CO2 Emissions during 2001–2030: A Case Study for Henan Province, China." Sustainability 11, no. 4 (February 22, 2019): 1176. http://dx.doi.org/10.3390/su11041176.
Повний текст джерелаАнотація:
In China, the industrial sector is the main contributor to economic development and CO2 emissions, especially for the developing regional provinces. This study employs the Logarithmic Mean Divisia Index (LMDI) approach to decompose industrial energy-related CO2 emission into eight factors during 2001–2015 for Henan Province. Furthermore, the future CO2 emissions under different scenarios (Business as Usual (BAU), Efficiency Improvement (EI), Structural Optimization (SO), R&D Input (RD), and Comprehensive Policy (CP) scenarios) over 2016–2030 are projected. The results indicate that among these factors, the economic output, R&D intensity, investment intensity, and energy structure are the drivers for increasing CO2 emissions over the entire period, with the contribution of 293, 83, 80, and 1% of the total CO2 emissions changes, respectively. Conversely, the energy intensity, R&D efficiency, and industrial internal structure can decrease CO2 emissions with contributions of –86, –163, and –108% to the changes, respectively. Under the five scenarios, CO2 emissions in 2030 will reach 1222, 1079, 793, 987, and 638 Mt with an annual growth rate of 4.7%, 3.8%, 1.8%, 3.3%, and 0.4%, respectively. In particular, the CO2 emission peak for SO and CP scenarios is observed before 2030. Finally, some policy implications are suggested to further mitigate industrial emissions.
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Wang, Qing, and Yuhang Xiao. "Has Urban Construction Land Achieved Low-Carbon Sustainable Development? A Case Study of North China Plain, China." Sustainability 14, no. 15 (August 1, 2022): 9434. http://dx.doi.org/10.3390/su14159434.
Повний текст джерелаАнотація:
The rapid expansion of urban construction land (UCL) provides a guarantee to support rapid economic development and meet the social needs of urban residents. However, urban construction land is also an important source of carbon dioxide emissions. Therefore, it is of great research value to investigate the relationship between UCL and carbon emissions in depth. Based on this, using panel data of 57 cities in the North China Plain from 2007 to 2018, the study found that there is a strong positive correlation between UCL and CO2 emissions. It can be seen that the expansion of UCL is an important source of CO2 emissions. On the basis of this research conclusion, first, this paper uses the Tapio decoupling model to analyze the decoupling relationship between UCL and carbon emissions in the North China Plain. Then, the spatial autocorrelation analysis was applied to explore the spatial correlation characteristics of the carbon emission intensity of UCL in cities in the North China Plain. Finally, using the GTWR model to analyze the influencing factors of the carbon emission intensity of UCL, the following conclusions were drawn. In 2007–2015, the decoupling relationship performed well, but it deteriorated significantly from 2015 to 2018; in addition, there was a significant positive spatial correlation of carbon emission intensity of UCL. Various influencing factors have a significant impact on the carbon emission intensity of UCL, for example, the urbanization rate, industrial structure, economic development level, and population density have a positive impact, and environmental regulations, foreign investment intensity, land use efficiency and greenery coverage have a negative impact. The research results of this paper provide a scientific basis for making decisions and optimizing pathways to achieve carbon emission reduction from UCL in the North China Plain, as well as certain reference values for other regions to achieve low-carbon development of UCL. This is significant for exploring the optimal solution of land and carbon emissions and building a harmonious human–land relationship.