Carbon Footprints and Embodied Carbon Flows Analysis for China’s Eight Regions: A New Perspective for Mitigation Solutions

The climate change is a major global problem, due to releasing the greenhouse gases (GHG) to the atmosphere at an alarming rate. The main constituent of GHGs is carbon dioxide (CO2) and impact of all other gases is measured in relation to CO2. Hence, GHG emissions are referred to as carbon dioxide equivalent or carbon emissions or carbon footprint (CFP). Transport is a major human activity which contributes to the emission of GHGs. In Sri Lanka there are no previous studies done to evaluate the carbon emissions from any road project. Unless mitigation measures are implemented, Sri Lanka’s GHG emissions will reach dangerous level. The objective in this study is to estimate the carbon footprint on Southern expressway (SEW) from Colombo to Galle due to vehicle movement under the operational stage.This study is mainly descriptive, based on secondary data. Vehicle fleet data was collected from RDA, November 2011 to June 2014. The DEFRA carbon factors were used to calculate the emissions from each category of vehicles, according to the fuel types. This study was conducted using distance based method using DEFRA guidelines. Distance between each interchange was considered according to the data gathered form RDA. ISO guide lines were used to develop the methodology and DEFRA guide lines for emission factors. Total Carbon Foot Print = Total Distance Travelled (km) x Emission Factor (kgCO2e / km) The total CFP for the expressway is 130,793.01 t CO2e for the period 2011 to 2014 and category one vehicles are the highest contributors of carbon emission (91%) in the SEW. The average GHG emission per day has increased by 47% from 2011 to 2014 and if continued so, carbon emissions on the SEW will increase to 221.2 tCO2e per day, leading to an increase of 135.77% from the base year (2011). The study showed that the emission per km between Kahathuduwa-Gelanigama interchanges, is highest among interchanges. The annual increase of the emission by 2014 indicates that the SEW has become popular. The yearly increase of traffic can drastically reduce these advantages unless steps are taken either to reduce this trend of increasing the vehicle emission or to widen the expressway. Since emissions between Kahathuduwa to Gelanigama is higher than others, it is evident that the usage of the expressway around this area is high and authorities to consider development of road infrastructure external to the SEW in this area. Keywords: Carbon footprint, Climate change, Southern expressway, Green house gases, Carbon dioxide

A significant disparity exists in the living conditions and lifestyles of people living in rural and urban India. Based on geographical location and lifestyle, an individual's contribution to the global carbon footprint has been estimated in this study. This study examines the household carbon footprint in different rural and urban regions of West Bengal. It analyzes the difference of contribution of the factors based on one’s geographical location and how an individual's socioeconomic status affects carbon footprint. Data on the consumption of goods and services resulting in GHG emissions was gathered at the household (N=243) level through a questionnaire survey in different districts of West Bengal. The average carbon footprint in rural areas is estimated to be 0.56 tones CO2e per capita per year. In urban areas, it is 2.33 tones CO2e per capita per year significant difference is found in the annual per capita carbon footprint (household, travel, lifestyle, and total carbon footprint) among the various income groups of rural and urban areas. Another significant difference is found among different monthly expenditure categories between urban and rural households. Based on the information given by the respondents of the rural and urban areas the difference regarding electricity consumption, travelling details, monthly expenditure category, type of energy used, cooking fuel, annual household, travel, and lifestyle carbon footprint have been discussed graphically. This study looked at the sectoral contribution (activity-wise, e.g., cooking, transportation, etc.) and the rural-urban disparity in the individual carbon footprint.

Carbon footprint of agricultural pesticides on main crops in China from 2011 to 2023.

China is the largest carbon emitter in the world; thus, reducing carbon emissions while maintaining economic growth has become an important issue. Within the context of carbon neutrality strategies, calculation of the carbon footprint and embodied carbon transfer can help policymakers formulate reasonable carbon reduction plans. The multi–regional input–output (MRIO) model can clarify carbon flow pathways between regions, and social network analysis (SNA) can comprehensively evaluate the different positions of individual sectors. Combining these two approaches, the specific characteristics of carbon emissions in complex production and trade relationships can be analyzed. China has become the world’s top total carbon emitter, and the Hanjiang River basin (HJRB) constitutes an important economic link between the developed and less developed regions of China. Studying carbon emissions in the HJRB can provide a reference for other, similar regions and is vital for the realization of China’s carbon emission reduction targets. This paper examines the carbon footprint and embodied carbon emission transfer among three provinces and 12 sectors in the HJRB during different periods and identifies the key industries in the carbon transfer process. The results indicate that (1) the total carbon footprint in the HJRB exhibits an increasing trend. Energy-based Shaanxi Province exhibits the highest growth rate of the carbon footprint, agriculture-based Henan Province shows a decreasing trend, and consumption-based Hubei Province displays the lowest carbon footprint intensity. (2) There are differences in the carbon emission coefficient and final consumption rate among various sectors; construction, metal processing and metal and non-metallic products, processing and manufacturing of petroleum, coking, nuclear fuel, chemical products, and other services are the sectors accounting for a high proportion of emissions. (3) The more obvious the supply relationship is, the higher the flow of embodied carbon emission transfer between sectors. (4) Energy-based regions transfer large amounts of fossil energy, electricity, steel and coal resources to developed regions and simultaneously assume more of the carbon reduction pressure imposed on developed regions. (5) The key industries within the embodied carbon emission transfer network notably control the carbon emissions of other industries and can provide breakthroughs to achieve challenging carbon emission reduction targets.

This study aims to provide a scientific basis to address the strategies for sustainable development of urban tourism industry. By using the Life Cycle Assessment method, it decomposes tourism activities into seven different functional units (different tourism activities)-transportation, catering, accommodation, sightseeing, shopping, entertainment and waste disposal-based on the expression of services provided by tourism activities, and determine the boundary range of each different functional unit in terms of the pathways and the functional orientation of the products (resources and energy) provided by the services of each functional unit. A “bottom-up” model is then constructed to measure the carbon footprint of tourism. Based on data collected from various sources for the period 2014–2019, it compares the composition and differences of domestic and international tourists’ carbon footprints in Chenzhou City, one of inland mountainous regions of central China, through several steps, including target and scope definition, inventory analysis, impact evaluation and life cycle interpretation. Results show that domestic tourists contributed more than 90% of the total annual carbon footprints to the city, ranging from 76.8809 × 106 kg to 194.6067 × 106 kg. Transportation is the dominant category, accounting for over 80% of the total carbon footprints. The study suggests that optimizing tourism resources, reducing transportation distances, and switching to low-carbon modes can effectively reduce the tourism carbon footprints in Chenzhou and similar regions. This study reveals the structural characteristics of the tourism carbon footprint and its influencing factors and provides valuable insights for policy development involving energy saving and low carbon tourism, thus enhancing the long-term sustainability of tourism development in an urban tourism destination like Chenzhou.

Nowadays, low greenhouse gas (GHG) emission is expected at wastewater treatment plants (WWTPs). However, emission quantification and evaluation still faces difficulties related to data availability and uncertainty. The objective of this study was to perform carbon footprint (CF) analysis for two municipal WWTPs located in northern Poland. Slupsk WWTP is a large biological nutrient removal (BNR) facility (250,000 PE) which benefits from on-site electricity production from biogas. The other studied plant is a medium-size BNR facility in Starogard (60,000 PE). In this WWTP, all the required electricity was provided from the grid. Both wastewater systems were composed of activated sludge, with differences in the nutrient removal efficiency and sludge treatment line. The CF calculations were based on empirical models considering various categories of input parameters, afterwards summing up the emissions expressed in CO2 equivalents (CO2e). After sensitivity analysis, significant contributors to GHG emissions were identified. The total specific CF of the Slupsk and the Starogard WWTP was 17.3 and 38.8 CO2e per population equivalent (PE), respectively. In both cases, sludge management, electricity consumption and direct emissions from wastewater treatment were found to significantly influence the CF. A substantial share of the total CF originated from indirect emissions, primarily caused by the energy consumption. This negative impact can be partially overcome by increasing the share of renewable energy sources. Reduction of over 30% in the total CF could be achieved while applying energy recovery from biogas by combined heat and power plants. Farmland and farmland after composting were found to be the most appropriate strategies for sludge management. They could create a CF credit (8% of the total CF) as a result of substituting a synthetic fertilizer. Reliable full-scale measurements of N2O emissions from wastewater treatment are recommended due to high uncertainty in CF estimation based on fixed emission factors (EFs). While applying the lowest and the highest N2O EFs reported in the literature, the total CF would change even by 2-3 times.

The impact of climate change is increasingly recognized as a major public health determinant. A life cycle assessment to determine the carbon emissions associated with open surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR) in the operating room (OR), and the cath lab (CATH) was performed. Total carbon footprint from SAVR (n = 10) and TAVR (n = 10 OR-TAVR, n = 10 CATH-TAVR) from March to September 2023 was calculated. Patients undergoing any other procedure at the time of SAVR or TAVR were excluded. A model for carbon footprints, measured in kilograms of CO2 equivalents (kg CO2e) was created following ISO14067 standards, based on primary data (materials, procedures, energies, in the pre-operative, operative, and post-operative setting). Reported footprints carry a coefficient of variation of 10% for totals and up to 25% for individual life cycle stages, as standard in carbon footprinting analysis. Median age for OR-TAVR, CATH-TAVR, and SAVR was 77 (range 65-91), 82 (7196), and 66 (51-79) years, respectively. Median Society of Thoracic Surgeons risk for same was 4.9, 2.8, and 1.4%, respectively. Ejection fraction was similar across groups. Total life cycle carbon footprint for OR-TAVR, CATH-TAVR, and SAVR was 280-340 kg CO2e, 290-360 kg CO2e, and 620-750 kg CO2e, respectively (P < .05 SAVR vs either TAVR). Post-operative intensive care unit and floor care accounted for the largest portion of the carbon footprint, including ∼170 kg CO2e for OR-TAVR (55% of total), 170 kg CO2e for CATH-TAVR (52% of total), and 405 kg CO2e for SAVR (59% of total) (P < .05 SAVR vs either TAVR). Of the total, intensive care unit length of stay was a large contributor to the carbon footprint, comprising ∼27% of OR-TAVR, 25% of CATH-TAVR, and 43% of the SAVR footprint. Approximate intraoperative carbon footprint was 100 kg CO2e for OR-TAVR, 103 kg CO2e for CATH-TAVR, and 241 kg CO2e for SAVR. The intraoperative footprint of SAVR was driven by biological waste, post-operative length of stay, and inhaled anaesthetic gases. The carbon footprint of SAVR is about twice as high as those from OR-TAVR or CATH-TAVR. These findings should potentially be considered when making population level decisions and guidelines moving into the future.

In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%–65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association (LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.

Developing more effective mitigation strategy to achieve the carbon reduction targets set by the Paris Climate Agreement requires to more comprehension of the driving factors of the decline in carbon emissions. Production-based and consumption-based carbon emission in Germany declined by 2.8% and 8.5% from 2000 to 2015, respectively. In this work, the key drivers behind decline in carbon emission in Germany is explored by combing the multi-regional input-output and structural decomposition analysis. The results show German carbon emissions has been undergoing structural changes. Although its production-based and consumption-based emission declined, German carbon footprint embodied in exports increased, and its share in the production-based carbon footprint also increased. Germany's embodied emission exports to emerging countries increased, although Germany's embodied carbon exports remained mainly concentrate in developed countries, especially in EU (over 40%) and USA (∼10%). The decomposition results revealed that economic scale effect was the leading contributor to increase in carbon footprint in Germany's trade, followed by the industrial structure effect of final demand. The effects of production structure on German's exports was less than its imports. The carbon intensity coefficient was the most significant factor of decreased carbon emission. Generally, the decreasing effect of carbon intensity coefficient exceeded the increasing effect of economic scale, which lead to carbon reduction. The example of Germany shows that improving carbon emission efficiency and optimizing production structure can reduce carbon emission without lowering economic growth.

The Belt and Road Initiative (BRI) provides a platform for developing countries with huge growth potentials, which may also face huge carbon emission pressure while achieving rapid economic growth. Given certain similarities in economic patterns and resource endowments, this study aims to trace carbon emission decoupling and decomposition of different countries/regions within the Belt and Road area and provide new insights into the drivers of carbon emission decoupling from both production- and consumption-based perspectives. Based on the multi-regional input-output modelling and Tapio decoupling decomposition, this study quantitatively analyzes the decoupling evolution and decomposition drivers of economic activities and carbon emissions in countries along the Belt and Road. From the results, the production-based carbon emissions of the Belt and Road countries was significantly higher than the consumption-based carbon emissions. The increasing rate in the production-based carbon emissions was also faster than the consumption-based one, with an increasing huge gap between the two sides. Regarding the spatial distribution of carbon emissions, the regions with huge amounts of carbon emissions mainly distributed in Russia, Iran, South Korea, and Saudi Arabia. When compared, the consumption-based carbon emissions of China and Russia were the highest, followed by those of the countries in Central Asia and West Asia. Compared with the production-based side, the decoupling rate of the Belt and Road countries was slower than the consumption-based one. The Belt and Road countries mainly maintained in the weak decoupling status, with the economic effect as the main driver in carbon emission growth, and the energy intensity effect as the dominated contributor in carbon emission reduction. Through exploring the decoupling and decomposition of production- and consumption-based carbon emissions within the Belt and Road countries, this study attempts to provide certain implications for the low-carbon transition and sustainable development within the countries along the Belt and Road.

Carbon footprint and embodied carbon transfer at city level: A nested MRIO analysis of Central Plain urban agglomeration in China

China’s carbon emission research started relatively late. In order to further enrich its related research, the study uses a carbon emission factor fusion building information model and a full life cycle method to calculate the building material carbon footprint of to evaluate the carbon emissions of selected projects. In the instance calculation, it was found that the total carbon footprint production during the operation performed the highest, at 56560.23 t CO2, accounting for 79.37% of the total carbon footprint output throughout the entire life cycle of the construction project. The total carbon footprint generated during the preparation phase of building materials was 11483.56 t CO2, accounting for 16.11% of the total carbon footprint output throughout the project life cycle. The total production of carbon footprint during the operation phase was the highest, at 56560.23 t CO2, accounting for 79.37% of the entire project life cycle. The output of carbon footprint during the dismantling and scrapping stage was 2245.8 t CO2, accounting for 3.15% of the total amount of life cycle assessment carbon footprint in the project. The total amount of carbon footprint generated in the early stage of the construction project was 1.28 t CO2, and the total amount of carbon footprint generated in constructing was 973.22 t CO2. The emission of carbon footprint accounted for 1.37% of the entire project life cycle. The obtained result data has a high degree of overlap with existing research results in China and has certain reference value.

The integrated planning of central and local emission reduction tasks is crucial for achieving sustainable economic development, and corporate ESG performance aligns with the principles of sustainable development, having become a prominent topic in academic research. This paper empirically investigates the impact of regional carbon emission reductions on the ESG performance of local enterprises from 2009 to 2021 using provincial carbon emission data from China. The findings indicate that regional carbon emission reductions significantly enhance the ESG performance of local firms. The underlying mechanism is that regional carbon emission reductions facilitate local enterprises obtaining green credit, attracting media coverage and green investors and thus improving ESG performance. Second, heterogeneity tests reveal that regional carbon emission reductions enhance the ESG performance of local firms more significantly in regions with stricter environmental regulations, within heavily polluted industries, and among less digitized enterprises. Finally, further analysis demonstrates that regional residents’ carbon emission reductions can enhance the ESG performance of local enterprises, with regional carbon emission reductions exerting a dual effect after improving ESG performance. The findings of this study provide valuable insights into the low-carbon development of various economic entities and the collaborative promotion of economic green transformation.

PDF HTML阅读 XML下载 导出引用 引用提醒 中国省际碳足迹广度、深度评价及时空格局 DOI: 10.5846/stxb201901010003 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家社会科学基金项目（17BJL105） Assessment of carbon footprint size, depth and its spatial-temporal pattern at the provincial level in China Author: Affiliation: Fund Project: National Social Science Foundation of China, No. 17BJL105 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:借鉴三维生态足迹方法构建了碳足迹广度、深度测算模型，对吸纳碳排放所占用的自然资本流量、存量进行区分，核算了2000-2016年中国30个省（市、自治区）碳足迹广度和碳足迹深度，并对其进行空间关联性分析。结果显示：①中国碳足迹广度受碳足迹和碳生态承载力的综合影响，由0.173 hm2/人升至0.329 hm2/人又降至0.301 hm2/人；碳足迹广度高值区集中于东北、西北和西南地区，其自然资本流量尚未完全占用，低值区集中于东部沿海和中部，其自然资本流量已不足以补偿碳排放。②2008年起中国碳足迹深度突破自然原长1，数值由1.04升至1.42又降至1.31；研究期内碳足迹深度始终处于自然原长1的有10个地区，高值区集中于东部沿海和中部，尤其是上海可达298.83，以存量资本耗竭为主且生态持续性弱。吸纳碳排放所占用的流量资本和存量资本存在地域互补性。③中国碳足迹广度、深度呈显著的空间正相关。碳足迹广度H-H集聚区分布于东北和西北，该类集聚有减弱趋势；碳足迹深度H-H集聚区主要分布于东部沿海且向中部扩散，该类集聚有增强趋势。通过引入碳足迹广度、深度两项指标对碳足迹的研究方法进行了深化和完善，在碳排放对生态环境影响规模的刻画和表达上取得了较优于传统碳足迹的评价结果。 Abstract:The relationship between carbon emissions and carbon absorption is unbalanced, and the carbon cycle system is facing tremendous ecological pressure at present in China. In this paper, a carbon footprint size and depth measurement model was firstly established based on the three-dimensional ecological footprint method. Then we used this model to distinguish the natural capital flow and stock occupied by absorbing carbon emissions. Furthermore, the carbon footprint size and depth of 30 provinces were calculated on the basis of energy consumption from 2000 to 2016. The spatial correlation of carbon footprint size (depth) was analyzed by spatial autocorrelation method. The results showed as follows:(1) The carbon footprint size in China was affected by carbon footprint and carbon ecological carrying capacity. It showed an increase from 0.173 hm2/person to 0.329 hm2/person and then decrease to 0.301 hm2/person. The high-value areas of carbon footprint size were the northeast, northwest and southwest regions where the natural capital flow was not yet fully occupied. The low-value areas were mainly distributed in the eastern coast and the central regions where the natural capital flow was insufficient to compensate for carbon emissions. (2) The provincial carbon footprint depth has exceeded the natural length of 1, and the depth increased from 1.04 to 1.42 and then decreased to 1.31 since 2008. The carbon footprint depth in 10 provinces was always at the natural length of 1 from 2000 to 2016. The high-value areas of carbon footprint depth were the eastern coast and the central regions where the natural capital stock was consumed by carbon emissions and the ecological sustainability was weak, while the highest depth occurred in Shanghai with 298.83. There existed the regional complementarity between natural capital flow and natural capital stock for absorbing carbon emissions. (3) Using the global spatial autocorrelation analysis, we found that carbon footprint size and depth showed the positive correlation and significant spatial agglomeration in all provinces of China. Through the local spatial autocorrelation analysis, the High-High agglomeration areas of carbon footprint size were mainly distributed in the northeast and northwest regions, and there was a tendency of number decrease of High-High agglomeration areas. Moreover, the High-High agglomeration areas of carbon footprint depth were mainly distributed in the eastern coastal areas and there was an obvious spread trend toward the adjacent regions. By introducing the two indexes of carbon footprint size and carbon footprint depth, the research methods of carbon footprint are further improved, and the evaluation results are more accurate and reasonable than the results by the traditional carbon footprint theory. 参考文献 相似文献 引证文献

This study aimed to estimate the carbon footprint of Philippine households from consuming various goods and services. Data from the Philippine Input-Output Table and Global Trade Analysis Project’s carbon emission coefficients were used to extract the carbon intensities of different economic sectors. The embodied carbon emission from different consumption items was estimated by tracing the associated emission down to its intermediate inputs used in the production. The total household carbon footprint was derived by summing up the carbon emission from each consumption category. Results showed that the highest carbon emitting goods consumed by households are related to expenditure on fuel, light and transportation while nondurable and recreation goods were the least carbon intensive. Different socio-economic characteristics of the households matter in explaining total household carbon footprint. By using non-parametric estimation, results showed a strong positive relationship between household carbon footprint and income but the effect varies across the distribution. This implies that further increases in carbon footprint are to be expected as households get richer. Policy makers should devise policies promoting green consumption or low-carbon lifestyle; else it is likely that households will be leading a carbon intensive lifestyle as they become more affluent.