Analysis of Carbon Emission Caused by Food Consumption in City and Rural Inhabitants in China

Agri-food evolution and carbon emissions in Chinese residential consumption: A life cycle analysis of urban-rural disparities and socioeconomic influences

This study examines the nexus between business strategy and carbon emissions by utilising a dataset of U.S. firms from 2007 to 2020. It focuses on two broad types of firms, that is, prospectors and defenders. Regarding carbon emissions, we consider total emissions (Scope 1 & 2), direct emissions (Scope 1) and indirect emissions (Scope 2). The results reveal a significant association between business strategy and total carbon emissions as well as direct carbon emissions. Notably, the results suggest that prospectors, compared to defenders, display higher levels of total and direct carbon emissions. Our findings contribute to the debate on whether prospectors in developed countries mismanage sustainability issues. The study offers valuable insights into the interplay between business strategy and carbon emissions and provides empirical evidence that business strategy is an important determinant of total and direct carbon emissions.

Hydropower is the largest renewable source of electricity generation, the carbon emissions of which have attracted a lot attention. However, the system boundaries of existing studies are either incomplete or inaccurate. Therefore, this study provides a systems accounting framework for evaluating both the direct and indirect carbon emissions from a hydropower plant. It is based on the hybrid method as a combination of the process analysis and the input-output analysis. To demonstrate the framework, a case study for a typical pumped storage hydropower plant (NPSHP) is carried out. The total carbon emissions are estimated as 5828.39 kt in the life-cycle of the case system. The end-of-use stage causes the largest carbon emissions (38.4%), followed by the construction stage (34.5%), the operation stage (25.6%), and the preparation stage (1.5%). The direct carbon emissions are mainly released from sediments in the end-of-use stage and the surface of reservoirs in the operation stage (94.8%). The indirect carbon emissions are 2.8 times higher than the direct carbon emissions. The material, machinery, energy, and service inputs respectively account for 7.1%, 14.7%, 15.9%, and 62.3% of the total indirect carbon emissions by the case system. The indicator of EGOC (electricity generation on carbon emission) for the NPSHP is calculated as 26.06 g CO2-eq./kWh, which is lower than that of most other power plants.

The indirect carbon emission embodied in the intermediate input is also an important indicator of assessing a producer's carbon emissions. Structural analysis of indirect carbon emissions is helpful to understand the responsibilities between producers and pay efforts to key areas. The aim of this study is to analyze indirect carbon emissions embodied in intermediate input between sectors and explore the distribution structure of indirect carbon emissions flow network (namely, ICEFN). Based on the modified input-output model and complex network theory, this study constructed four directed and weighted ICEFNs with 28 sectors from 1997 to 2012. The results show that indirect carbon emissions between sectors are significantly higher than direct carbon emissions, accounting for nearly 70% of the total carbon emissions of China. Second, we analyzed the embodied carbon emission intensity (namely, ECI) of each sector. Although the ECI has been decreasing over time, the decrease has increasingly diminished, which indicates that the additional carbon emission reductions are more difficult. Third, we identified the key sectors which play different roles in the ICEFNs. Meanwhile, we studied the key paths which show more closed relationships between some sectors in ICEFNs. Finally, based on the above analysis, we made policy recommendations.

The standard of living has significantly risen along with ongoing economic progress, but CO2 emissions have also been rising. The reduction in CO2 resulting from the daily activities of residents has become a crucial priority for every province. A relevant study on the carbon emissions of Hebei Province residents was conducted for this publication, aiming to provide a theoretical basis for the sustainable development of Hebei Province. The first part of the article calculates the carbon emissions of Hebei Province people from 2005 to 2020 using the emission factor method and the Consumer Lifestyle Approach (CLA). Secondly, the Logarithmic Mean Divisia Index (LMDI) decomposition approach is used to assess the components that influence both direct and indirect carbon emissions. Finally, the scenario analysis approach is employed in conjunction with the LEAP model to establish baseline, low-carbon, and ultra-low-carbon scenarios to predict the trend of residents’ carbon emissions in Hebei Province from 2021 to 2040. The results show that the total carbon emissions of residents in Hebei Province from 2005 to 2020 rose, from 77.45 million tons to 153.35 million tons. Income level, energy consumption intensity, and population scale are factors that contribute to the increase in direct carbon emissions, while consumption tendency factors have a mitigating effect on direct carbon emissions. Economic level, consumption structure, and population scale factors are factors that contribute to the increase in indirect carbon emissions, while energy consumption intensity and energy structure factors have a mitigating effect on indirect carbon emissions. The prediction results show that under the baseline scenario, the cumulative residents’ carbon emissions in Hebei Province will not reach a zenith from 2021 to 2040. However, under the low-carbon situation, the carbon emissions of residents in Hebei Province will peak in 2029, with a peak of 174.69 million tons, whereas under the ultra-low-carbon scenario, it will peak in 2028, with a peak of 173.27 million tons.

The accelerated growth of the global economy has given rise to a multitude of environmental concerns that demand immediate attention. At this juncture, the total global carbon emissions are exhibiting a gradual increase. China, the United States, India, Russia, and Japan represent the top five countries in terms of global carbon emissions, collectively accounting for approximately 60% of the global total. Of these, China’s carbon emissions are the highest in the world, representing over 30% of the global total. As urbanization accelerates, the carbon emissions from urban agglomerations constitute a substantial share of the nation’s total emissions, rendering the carbon emissions of urban clusters a critical issue. In the context of China’s urban agglomerations, the Beijing–Tianjin–Hebei region, due to factors such as industrial structure, accounts for a relatively high proportion of carbon emissions, approximately 11% of the national total. The future trajectory of carbon emissions in the Beijing–Tianjin–Hebei region will significantly impact the high-quality development of the entire urban cluster. Consequently, research on carbon emissions in the Beijing–Tianjin–Hebei region is of vital importance. This paper takes the carbon emissions of the power industry in the Beijing–Tianjin–Hebei region as the research subject, analyzes its carbon emissions status, and builds a multi-regional input–output model for the Beijing–Tianjin–Hebei region based on the input–output tables and carbon emissions data of each province. This study explores the key influencing factors of carbon emissions from the power industry in this region from 2012 to 2017 and analyzes the carbon emissions transfer and structural evolution from the perspective of the region and the industry to clarify the carbon reduction responsibilities of the Beijing–Tianjin–Hebei region and provide references and recommendations for the formulation of regional collaborative emission reduction policies. The results show that the direct carbon emissions from the power industry in the Beijing–Tianjin–Hebei region account for a higher proportion compared to the indirect carbon emissions it generates by driving other industries. Industries with relatively high indirect carbon emissions in the key path include coal mining and selection, equipment manufacturing, transportation, services, etc. The capital input process from Tianjin and Hebei to Beijing is accompanied by a relatively high carbon transfer. Promoting the widespread adoption of carbon emission reduction technologies will have an effective suppressive effect on carbon emissions in the Beijing–Tianjin–Hebei region, especially in Hebei; Beijing and Tianjin should pay attention to the stimulating effect of increased final demand on carbon emissions; the transfer of carbon emissions between regions and industries shows a downward trend as the power sector undergoes transformation.

The carbon emissions of three typical processes （AAO, MSBR, and oxidation ditch） were systematically analyzed from the perspective of the whole wastewater treatment process based on the annual data of eleven urban small and medium-scale WWTPs in the year 2022, and the effects of different influent characteristics （TP, TN, BOD5, COD, influent volume, and COD/TN） on the carbon emissions were studied by using the partial least squares structural equation modeling （PLS-SEM） method. The results showed that indirect carbon emissions dominated the total carbon emissions of small and medium-scale WWTPs （69.5%）, and carbon emissions from electricity consumption were the largest source （43.1%）； the evaluated carbon emission intensity of the MSBR process [（0.363±0.007） kg·m-3] was lower than that of the AAO process [（0.439±0.099） kg·m-3] and oxidation ditch process [（0.396±0.025） kg·m-3], which had a superior level of low-carbon operation. The carbon intensity of the various components of small and medium-scale WWTPs was as follows： biological treatment （58.7%） > sludge dewatering+deodorisation （17.9%） > deep treatment （17.3%） > pre-treatment （6.1%）； the biological part accounted for 51.5%, 53.9%, and 73.1% of the total carbon intensity of the three processes, respectively. In addition, the influent COD concentration and COD/TN had significantly positive （path coefficients of 0.584 and 0.5） and negative （path coefficient of -0.416） impacts on the direct carbon emissions （N2O and CH4, respectively）, and the influent concentrations of TN, BOD5, and TP had significantly positive effects on the indirect carbon emissions （electricity consumption） and indirect carbon emissions （material consumption）, with path coefficients of 0.078, 0.537, and 0.5, respectively. Carbon reduction in small and medium-scale WWTPs can be carried out by reducing carbon intensity, decreasing carbon operation levels, and strengthening government management.

Household carbon emissions (HCEs) in urban communities are significant sources of China’s total carbon emissions and contribute to global warming and climate change dramatically. This study aims to estimate the HCEs and investigate their influential factors based on a total of 185 household survey data collected from three typical types of urban communities in Beijing: traditional communities, unit communities, and commercial housing communities with the application of the consumer lifestyle approach analysis and econometrics model. The results show that unit communities contribute to the highest direct carbon emissions and the commercial housing communities produce the most indirect carbon emissions, with the traditional communities emitting the lowest carbon emissions both directly and indirectly. The highest direct carbon emissions of households are found in unit communities at 723.79 kgCO2 per month, followed by commercial communities at 580.01 kgCO2, and finally the traditional communities with 526.44 kgCO2 direct carbon emissions monthly. And the highest monthly indirect carbon emissions of households are found in commercial communities at 707.70 kgCO2, followed by unit communities at 669.38 kgCO2, and finally with 554.85 kgCO2 indirect carbon emissions monthly in traditional communities. It concludes that the community type affects HCE characteristics and their driving factors significantly. Household income, household population, and the ownership of cars increase HCE in more than one type of community. Scientific research work-related population, community environment satisfaction, housing area have positive effects, while community convenience has negative impacts on HCEs in one certain type of community. Policy implications tailored to general and specific community types are proposed as the guidance of carbon reduction and community transformation finally. This study contributes to the understanding of the impact of community attributes on HCEs and proposes some methods for microregional carbon emission reduction and the ecological transformation of urban communities.

With accelerating urbanization, building sector has been becoming more important source of China’s total carbon emission. In this paper, we try to calculate the life-cycle carbon emission, analyze influencing factors of carbon emission, and assess the delinking index of carbon emission in China’s building sector. The results show: (i) Total carbon emission in China’s building industry increase from 984.69 million tons of CO2 in 2005 to 3753.98 million tons of CO2 in 2013. The average annual growth rate is 18.21% per year. Indirect carbon emission from building material consumption accounted to 96–99% of total carbon emission. (ii) The indirect emission intensity effect was leading contributor to change of carbon emission. The following was economic output effects, which always contributed to increase in carbon emission. Energy intensity effect and energy structure effect took negligible role to offset carbon emission. (iii) Delinking index show the status between carbon emission and economic output in China’s building industry during 2005–2006 and 2007–2008 was weak decoupling; during 2006–2007 and during 2008–2010 was expansive decoupling; and during 2010–2013 was expansive negative decoupling.

Chapter 17 - Analysis of carbon emissions in composting and vermicomposting of excess sludge

China is undergoing rapid urbanization, enlarging the construction industry, greatly expanding built-up land, and generating substantial carbon emissions. We calculated both the direct and indirect carbon emissions from energy consumption (anthropogenic emissions) in the construction sector and analyzed built-up land expansion and carbon storage losses from the terrestrial ecosystem. According to our study, the total anthropogenic carbon emissions from the construction sector increased from 3,905×10(4) to 103,721.17×10(4) t from 1995 to 2010, representing 27.87%-34.31% of the total carbon emissions from energy consumption in China. Indirect carbon emissions from other industrial sectors induced by the construction sector represented approximately 97% of the total anthropogenic carbon emissions of the sector. These emissions were mainly concentrated in seven upstream industry sectors. Based on our assumptions, built-up land expansion caused 3704.84×10(4) t of carbon storage loss from vegetation between 1995 and 2010. Cropland was the main built-up land expansion type across all regions. The study shows great regional differences. Coastal regions showed dramatic built-up land expansion, greater carbon storage losses from vegetation, and greater anthropogenic carbon emissions. These regional differences were the most obvious in East China followed by Midsouth China. These regions are under pressure for strong carbon emissions reduction.

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

Some problems exist in the current carbon emissions benchmark setting systems. The primary consideration for industrial carbon emissions standards highly relate to direct carbon emissions (power-related emissions) and only a portion of indirect emissions are considered in the current carbon emissions accounting processes. This practice is insufficient and may cause double counting to some extent due to mixed emission sources. To better integrate and quantify direct and indirect carbon emissions, an embodied industrial carbon emissions benchmark setting method is proposed to guide the establishment of carbon emissions benchmarks based on input-output analysis. This method attempts to link direct carbon emissions with inter-industrial economic exchanges and systematically quantifies carbon emissions embodied in total product delivery chains. The purpose of this study is to design a practical new set of embodied intensity-based benchmarks for both direct and indirect carbon emissions. Beijing, at the first level of carbon emissions trading pilot schemes in China, plays a significant role in the establishment of these schemes and is chosen as an example in this study. The newly proposed method tends to relate emissions directly to each responsibility in a practical way through the measurement of complex production and supply chains and reduce carbon emissions from their original sources. This method is expected to be developed under uncertain internal and external contexts and is further expected to be generalized to guide the establishment of industrial benchmarks for carbon emissions trading schemes in China and other countries.

Structural decomposition analysis of carbon emissions from residential consumption in the Beijing-Tianjin-Hebei region, China

This manuscript examines the driving forces of carbon emissions in China’s tourism industry. Tourism carbon emissions are estimated by constructing China’s Economic-Environmental Accounts (EEA). Analysis is divided into five-time intervals and specifically examines intensity, scale, structure, and technology. Following index and structural decomposition methods, changes in tourism carbon emissions were segmented into sixteen economy-wide and tourism-specific driving forces. Results demonstrate that direct and total tourism carbon emissions compose 0.7% and 2.7% of total carbon emissions in China. Analysis revealed the positive driver of tourism emissions was domestic tourists, representing 140.4% increase in direct and 263.4% increase in total tourism carbon emissions. Modelling identified energy intensity as the main negative driver in total and direct tourism carbon emissions, especially for national economic sectors (−208.6%) and non-transport tourism sectors (−33.8%). Future research should focus on the measurement and implementation of mitigation policies for domestic tourism emissions.