Carbon Footprint Of Consumption Research Articles

Carbon footprint of household meat consumption in China: A life-cycle-based perspective

The food industry accounts for 25% of global carbon emissions, of which animal food accounts for 57%. Therefore, the reduction of carbon emissions from meat products consumption is one of the important issues of global climate governance. This study calculates and compares the carbon footprints of major meat products in China at various stages of production and consumption and the trends of their development based on life cycle assessment and questionnaires, which will help to understand the emission characteristics and reduction potential of the food sector in China. The results show that the main stages of the life cycle and the size of the total carbon footprint of the meat category for the same unit standard exhibit the following order: beef > mutton > pork > poultry (chicken and duck) > seafood (fish and shrimp); the farming factor has a greater impact on carbon emissions, followed by transportation and a smaller impact at the meal-making stage; from 2010 to 2021, China's total meat products consumption and carbon emissions continue to increase, but the structure of meat consumption is gradually diversified and balanced, and the trend of carbon emissions shows a slowdown in growth with structural changes. Finally, based on the results of the study, this paper proposes countermeasures for meat production and consumption.

Estimating the carbon footprint of Mexican food consumption based on a high-resolution environmentally extended input-output model.

Increased global attention is being paid to the food-health-climate trilemma. In this study, we evaluate the climate impacts of Mexico's food consumption patterns by creating a high-resolution (262 sectors) Environmentally Extended Input-Output (EEIO) model called MXEEIO. We focus on the differences between food away from home (FAFH) and food at home (FAH) and compare Mexico's results with those of the USA. The results show that the main components of food spending in Mexico were meat, baked products, and beverages, raising concerns about their potential negative health effects if consumed excessively. Mexico's total greenhouse gas (GHG) emissions from food consumption were estimated at 149 million metric tons (MMT) in 2013, as opposed to 797 MMT for the USA. Meat and dairy products were the main contributors to Mexico's food-related GHG emissions, accounting for 57% of total emissions. Mexico spent a much smaller proportion of food-related income on FAFH than the USA (13% vs. 52%), suggesting great potential for growth as Mexico's per capita GDP continues to rise. Detailed contribution analysis shows that reducing Mexico's food-related GHG emissions would benefit most from a transition to low-carbon cattle farming, but mitigation efforts in other sectors such as crop cultivation and electricity generation are also important. Overall, our study underscores the significance of food-related GHG emissions in Mexico, especially those from meat and dairy products, and the mitigation challenges these sectors face.

The carbon footprint of fast fashion consumption and mitigation strategies-a case study of jeans

Fast fashion is driving the continued growth of the fashion industry's carbon emissions. Understanding how fast fashion consumption exacerbates carbon emissions is critical to guide mitigation strategies for the fashion industry. Taking jeans, a typical fast fashion product as an example, this study developed an LCA model to assess the carbon footprint of fast fashion consumption at global and national levels, and mitigation potentials of product service systems-related scenarios were then explored. Results show that the carbon footprint of fast fashion consumption is 2.50 kgCO2e/one wear jeans, 11 times higher than that of traditional fashion consumption. Jeans production and cross-broad transportation contributed 91 % of the carbon footprint of fast fashion consumption. Developed countries have a 53 % higher per capita carbon footprint of fast fashion consumption than developing countries. The second-hand trading model has the highest mitigation potential, reducing carbon emissions by 90 %. This study proposed an analytical framework for the carbon footprint of fast fashion consumption, which provides the basis for the environmental footprints of fast fashion products. Our findings provide insights into the carbon footprints of traditional and fast fashion consumption and strategies for the transition to circular fashion.

Reducing carbon footprint of typical coffee consumption from the whole lifecycle viewpoint

With around two billion cups consumption daily worldwide, the coffee industry faces pressures to improve its sustainability. Currently, coffee culture has gradually integrated into the lives of global people. However, very few studies have focus on how to reduce its lifecycle carbon emissions. This study is designed to establish a scientific benchmark to evaluate and compare the carbon footprint of four kinds of coffee (hot Americano, iced Americano, hot latte coffee, and iced latte coffee) from two coffee shops (shop T and shop O) in Macao, investigate the major contributors or factors, and provide some effective measures for coffee carbon emissions mitigation. The results shows that the unit carbon footprint of iced latte (0.86 kg CO2e) is higher than the other three coffees (hot latte-0.84 kg CO2e, iced Americano-0.29 kg CO2e, hot Americano-0.07 kg CO2e). From the perspective of life cycle, the raw material acquisition phase has the largest carbon emissions, accounting for 69.44%–96.80% of the total carbon emissions. Among them, milk and plastic cups consumption are the two main source of high carbon emissions. Further analysis shows that the replacement of milk and plastic cups with oat milk and oat cups reduces carbon emissions by 69.57%. For the whole Macao, carbon emissions of the ideal scenario will be about 3721.87 ton CO2e in 2050—43.98% less than the baseline scenario increase.

Geospatial assessment of the carbon footprint of water and electricity consumption in residential buildings in Doha, Qatar

The process of estimating the carbon footprint (CF) has become a key method for managing greenhouse gas (GHG) emissions, guiding strategies for emission reduction and validating those strategies. Given the complexity of quantifying total lifecycle emissions in residential buildings, this study delves into assessing the CF focusing on water and electricity consumption in two types of residential buildings: mainly residential villas and residential flats. This assessment was carried out in Doha City, Qatar, using data on water and electricity consumption from 2017 to 2020. The study employs the Multi-Regional Input-Output Life Cycle Assessment (MRIO-LCA) model to calculate and convert the water and electricity consumption data into the CF of these buildings. Further, the study employs various methods for statistical and spatial statistical analysis of CF emissions, including geographically weighted regression (GWR), Ordinary Least Squares (OLS), and hotspot and cold spot assessments. The annual electricity CF emissions from these buildings are approximately 7 MtCO2 equivalent, with residential villas contributing about 83% of this total. Concurrently, the annual average water CF in residential buildings is around 0.06 MtCO2 equivalent, predominantly attributed to villas. The findings highlight the substantial impact of residential structures, particularly villas, on the city's overall CF emissions. Furthermore, the findings underscore the significant impact of residential structures, especially villas, on Doha's CF emissions, revealing a marked seasonal increase, particularly during the summer months and a notable spike in 2020. The spatial analysis of CF emissions reveals consistent spatial clustering patterns across different seasons in 2020. Elevated CF emissions from villas are concentrated in Doha's central, northern, and northeastern regions, while cold spots are predominantly in the eastern and southern areas. Understanding CF in residential settings is crucial for developing strategies to reduce emissions, enhance energy efficiency, and address climate change. This research helps inform targeted interventions for more sustainable residential energy and water use, aligning with broader environmental and climate goals.

Structural Characteristics of the Household Carbon Footprint in an Aging Society

The aging population has posed a challenge to China’s carbon neutrality pledge. To study the household carbon footprint in an aging society, this paper has combined the age-specific consumption pattern and environmental input-output life cycle assessment (EIO-LCA) to calculate the carbon footprint of household consumption across age groups, and then identified the key pathways of carbon emissions via structural path analysis (SPA). Results indicate that the elderly contribute 11.65% to total consumption-based carbon emissions. The working group (ages 15–64) has the highest average carbon footprint (0.85 tCO2e), while the elderly group (ages 65 and above) has the lowest average carbon footprint (0.82 tCO2e). Urban households of all ages have a higher carbon footprint than rural households. Housing and food are the dominant sources of the elderly carbon footprint. Notably, the production and distribution of electric power and heat power sector associated with housing energy consumption plays a leading role in the carbon emissions pathways of elderly consumption. Measuring the carbon footprint of older people can support policy designs and decision making in key sectors along the supply chain, and further encourage low-carbon lifestyles among China’s elderly. Additionally, the findings of this study have broad applications, especially for developing countries undergoing demographic transitions.

Carbon footprint of residents' online consumption in China

Although China boasts the largest online consumption market, there has been no prior case study addressing the carbon footprint of online consumption. The rapid expansion of online consumption has posed a challenge to China's carbon neutrality pledge. This paper aims to fill this crucial gap by conducting a pioneering study based on survey data collected from 6000 households. To achieve this goal, this study has combined provincial Multi-Regional Input-Output (MRIO) tables with residents' online consumption calculated by the Non-Linear Optimization model, and applied the Environmental Input-Output Life Cycle Assessment (EIO-LCA) model to calculate the carbon footprint of residents' online consumption. Result indicated that the average carbon footprint of residents' online consumption was 0.46 tCO2e in 2016, accounting for 12.30% of the total consumption carbon footprint. Notably, high-emission regions include Beijing-Tianjin, Jiangsu-Zhejiang-Shanghai, and Hubei. Moreover, the dominant sources of the online consumption carbon footprint were electricity, food, clothing, and beauty products. Furthermore, we found that the carbon footprint of online consumption tends to increase with income, with differential income elasticities observed for various products and income groups. We also quantified the average expenditure-based life-cycle carbon coefficient for parcel delivery (0.22 kgCO2e/yuan) and conducted two scenario analyses to assess its impact. From a policy perspective, our research on measuring the carbon footprint of residents' online consumption can provide valuable insights for designing future policies and decisions related to e-commerce and key sectors along the supply chains. Additionally, it will further encourage Chinese residents to adopt low-carbon lifestyles.

Potential Carbon Footprint in Food System Based on Household Vegetable Consumption in Yogyakarta

Consumption is one part of the food system in a region. Food is a basic need that cannot be separated from various activities. Food systems account for about a quarter of the global carbon footprint, indicating that every activity in it will impact the environment. This study aims to explain the potential carbon footprint resulting from the vegetable consumption commodities in Yogyakarta as part of the food system. The city of Yogyakarta became the research area with the consideration that this area is an urban area with a high population density, a high proportion of waste transported to the final landfill, and the composition of organic waste, which is more than half the total volume of waste. This study uses secondary data from the Socio-Economic Survey (Susenas) from the BPS-Statistics Indonesia and relevant literature. A quantitative descriptive analysis technique was used in this study to explain the vegetable food consumption of the people of Yogyakarta City. The potential for carbon footprint and food waste is calculated using emission factors that include production activities up to distribution. The results show that eggplant has the highest potential carbon footprint based on consumption data, with an average value of 0.25 kg CO2-eq/kg per household in one week. The average potential carbon footprint of vegetable consumption per household in one week in Yogyakarta City is 1.9 kg CO2-eq/kg produce. Potential food waste based on consumption activities has an average value of 0.15 kg per household in one week.

Determinants and spatial spillover of inter-provincial carbon leakage in China: The perspective of economic cycles

Measuring and analysing carbon leakage are the foundation for improving environmental responsibility. Based on the origin–destination approach and spatial spillover analysis, this study explores factors of China's inter-provincial carbon leakage. A multiregional input–output model was used to obtain the carbon footprint of consumption, and the factors of carbon leakage were assessed. The results revealed that the economic development and environmental regulations of the origin and destination have opposite effects on carbon leakage. Accordingly, provinces with high-energy intensity and abundant energy resources are mainly net-exported carbon, whereas provinces with underdeveloped secondary industries are mainly net-imported carbon. Notably, a positive spillover effect of energy intensity exists at the origin, and a negative one occurs at the destination. Moreover, a positive spillover affects the origin's energy output, and the surrounding areas' energy endowment can create favourable conditions for carbon leakage at the origin. Finally, corresponding policy suggestions are presented based on the above analysis.

Consumer concerns over food insecurity drive reduction in the carbon footprint of food consumption

Food security is increasingly a societal concern, also in developed economies. While originally developed through a nutritional lens, food security is also increasingly incorporating the environmental quality of diets. This study develops a Structural Equation Model to examine how consumers concerns over food insecurity – in terms of its impact on health and poverty – and environmental beliefs influence the carbon footprint of diets. Using data from a survey representative of the Italian population, this study shows that health-related food insecurity concerns increase the use of health motives when shopping for food, in turn reducing the carbon footprint of the diet. Conversely, poverty-related food insecurity concerns are associated to diets higher in carbon footprint, as they reduce health motives, and increase private shopping motives (e.g., taste, low price). Overall, the study highlights how shifts to more sustainable food systems require a better understanding of what motivate consumers to make more sustainable food choices.

The Dietary Carbon Footprint of Portuguese Adults: Defining and Assessing Mitigation Scenarios for Greenhouse Gas Emissions

The food chain is a large contributor to environmental pollution, especially greenhouse gas emissions, strongly associated with the consumption of animal-based proteins. The understanding of the negative environmental impacts of dietary habits by the population is of the utmost importance to provide the means to effect change to more sustainable eating patterns. The main purpose of this study was to assess the carbon footprint of animal protein consumption in Portugal, while also evaluating six mitigation scenarios aiming to lower greenhouse gas emissions through strategic changes to the animal protein consumption of current dietary habits. Overall, the carbon footprint associated with animal protein consumption is 2.63 kg CO2 eq/(cap⋅day) nationally and 28.4 t CO2 eq/month for the faculty canteen. Meat is by far the largest contributor to the carbon footprint in both cases, with beef being its “hotspot”. All scenarios showed significant reduction potentials, with values ranging from 16% (lower value for both the national case and the faculty canteen) to 71% (faculty canteen). In sum, substantial carbon footprint reductions can be attained if policymakers support the implementation of effective measures to promote a shift in the current animal protein consumption towards more sustainable eating habits.

The carbon footprint of household clothing washing in Shanghai during winter and spring based on empirical data

The consumer use phase of textiles and fashion contributes one-third to the carbon and water footprint, which is the highest for the life cycle assessment technique. However, it is only estimated by questionnaire or estimated data. We carried out a 180-day direct data tracking for three volunteer families with different demographic characteristics. The results show that consumer behaviors significantly influence the carbon and water scarcity footprint. Moreover, the uncertainty between the test data and the energy label estimated data is revealed. For family 1, the energy label-based electricity consumption carbon footprint is 0.57 kgCO2e, while the empirical data result is 2.79 kgCO2e. We proposed an improved carbon footprint evaluation method with the correction parameters, which can be estimated using the sampling data from the customers. The results show that applying actual data collection and building a database is necessary to reduce the astonishing errors, making error analysis possible and meaningful.

Connecting food supply chains

Connecting food supply chains

Machine Learning the Carbon Footprint of Bitcoin Mining

Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on a novel bottom-up approach, which (i) conform with recent estimates, (ii) lie within the economic model bounds while (iii) delivering much narrower prediction intervals and yet (iv) raise alarming concerns, given recent evidence (e.g., from climate–weather integrated models). We demonstrate how machine learning methods can contribute to not-for-profit pressing societal issues, such as global warming, where data complexity and availability can be overcome.

The carbon footprint of podcasts

Podcasting is a large and swiftly growing industry that entertains, informs, and connects massive audiences worldwide. Yet the environmental impacts of podcasting have received little attention. This paper examines the carbon footprint of podcasts through the energy usage of both distributing and consuming podcasts in Canada. Research into the carbon footprint of media consumption remains relatively sparse. Podcasts merit particular attention due to their unique patterns of digital distribution and consumption. Using statistics from major podcast websites and market research firms as well as data on device energy intensity, this paper finds that total distribution of podcasts within the first 30 days of new episodes generates approximately 99,000 kg of carbon dioxide. Within Canada, yearly podcast listening through smartphones, computers, and smart speakers emits approximately 90,000,000 kg of carbon dioxide. Further research may reveal wider impacts of podcasting on listener behaviour, broader media consumption, and other carbon-intensive activities that are complemented, compounded, or substituted by podcasts.

Examining the carbon footprint of rice production and consumption in Hubei, China: A life cycle assessment and uncertainty analysis approach

This study aimed to quantify greenhouse gas emissions derived from the production-consumption of rice in Hubei—a major rice-producing province in central China. This research employed primary and secondary data collection methods. Primary data sources included interviews and experimental observations from seven counties in Hubei collected from June 2016 to December 2016. Secondary data sources—including national datasets, inter-governmental reports, and peer-reviewed articles—were used to extract relevant data, such as emission factors, and national and provincial rice output. Life Cycle Assessment was employed to build a comprehensive inventory and map of the rice carbon footprint, including the following five stages: production inputs, farm management, growth period, processing and sale, and consumption. Uncertainty analysis was performed to validate the reliability of carbon footprint estimations. Results showed that the carbon footprint for every 1 ton of polished rice in Hubei ranged between 4.19–6.81 t CO2e/t and was 5.39 t CO2e/t on average. Greenhouse gas emissions were primarily produced from rice fields during the growth stage (over 60% of greenhouse gas emissions of the whole life cycle of rice), followed by the consumption stage, and the production and transportation of agricultural inputs. Uncertainty analysis estimations indicated acceptable levels of reliability. This study's results indicate that the production and consumption of rice is a significant contributor to agricultural carbon emissions in Hubei—consistent with national estimates that place China as the largest carbon dioxide emitter globally. This research provides further insight into future policies and targeted initiatives for the efficient use of low-carbon agricultural inputs for rice production and consumption stages in China.

Reducing Carbon Footprint Inequality of Household Consumption in Rural Areas: Analysis from Five Representative Provinces in China.

Household consumption carbon footprint and inequality reductions are vital for a sustainable society, especially for rural areas. This study, focusing on rural China, one of the fastest growing economies with a massive population, explored the carbon footprint and inequality of household consumption using the latest micro household survey data of 2018 linked to environmental extended input--output analysis. The results show that in 2018 in rural China, the average household carbon footprint is 2.46 tons CO2-eq per capita, which is around one-third of China's average footprint, indicating the large potential for further growth. Housing (45.32%), transportation (20.45%), and food (19.62%) are the dominant contributors to the carbon footprint. Meanwhile, great inequality, with a Gini coefficient of 0.488, among rural households is observed, which is largely due to differences in type of house built or purchased (explaining 24.44% of the variation), heating (18.10%), car purchase (12.44%), and petrol consumption (12.44%). Provinces, average education, and nonfarm income are among the important factors influencing the inequality. In the process of urbanization and rural revitalization, there is a high possibility that the household carbon footprint continues to increase, maintaining high levels of inequality. The current energy transition toward less carbon-intensive fuels in rural China is likely to dampen the growth rates of carbon footprints and potentially decrease inequality. Carbon intensity decrease could significantly reduce carbon footprints, but increase inequality. More comprehensive measures to reduce carbon footprint and inequality are needed, including transitioning to clean energy, poverty alleviation, reduction of income inequality, and better health care coverage.

Close the carbon loophole

Close the carbon loophole

The association between the carbon footprint and the socio-economic characteristics of Belgian households

Understanding the demand-side drivers and the distribution of greenhouse gas emissions is key to designing fair and effective environmental policies. In this study, we quantify the relationship between the carbon footprint of consumption and socio-economic characteristics of Belgian households. We use a dataset that combines household-level consumption data with an environmentally extended input-output model which quantifies the greenhouse gas emissions embedded in the supply chain of goods and services that households consume. We find that income and household size are the most important determinants of household consumption-related emissions. We also find the emission intensity of household consumption in the lower part of the income distribution is higher than that of richer households because poorer households spend a higher share on emission intensive products, especially energy.

Negligible impacts of early COVID-19 confinement on household carbon footprints in Japan

The rapid and extensive changes in household consumption patterns during the coronavirus disease 2019 (COVID-19) pandemic can serve as a natural experiment for exploring the environmental outcomes of changing human behavior. Here, we assess the carbon footprint of household consumption in Japan during the early stages of the COVID-19 pandemic (January–May 2020), which were characterized by moderate confinement measures. The associated lifestyle changes did not have a significant effect on the overall household carbon footprint compared with 2015–2019 levels. However, there were significant trade-offs between individual consumption categories such that the carbon footprint increased for some categories (e.g., eating at home) or declined (e.g., eating out, transportation, clothing, and entertainment) or remained relatively unchanged (e.g., housing) for others. Furthermore, carbon footprint patterns between age groups were largely consistent with 2015–2019 levels. However, changes in food-related carbon footprints were visible for all age groups since March and, in some cases, since February.