Household income and carbon footprint: evidence from Taiwan

Because of increasing concern about global climate change,there has been a growing research interest in carbon footprint analysis recent years.Carbon footprint analysis on urban energy use takes both direct carbon emissions and indirect emissions into account,so it has great significance for the in-depth analysis on nature process of carbon emissions and scientific formulation on carbon reduction plan.Taking Xiamen as a study area,the hybrid analysis method of carbon footprint was used to access the carbon footprint on the energy use of Xiamen city in 2009.Besides the direct carbon emissions from the urban energy end-use in traditional research,the indirect carbon emissions from the cross-boundary traffic and the embodied energy of key urban materials were also included.The direct emissions include emissions from direct energy use in industrial sector,commercial sector,household sector,transport sector and so on,which often called scope 1 by WRI/WBCSD.The direct emissions also include emissions due to electricity and steam purchases for the sector in the city,often called scope 2 emissions.And indirect carbon emissions,which called scope 3,incorporate the surface,sailing and airline travel′s emissions across cities and the embodied energy of key urban materials: food,water,fuel,and concrete.Research result showed that:(1) Direct carbon emissions from the energy use on the sectors inside the city boundary including industry,transport,commerce and so on,namely scope 1 and scope 2,only take up 64% of the total city carbon footprint.However,the indirect emissions from the cross-boundary traffic and embodied energy of key urban materials account for 36%,which has usually been ignored as scope 3.(2) In the direct emissions,the carbon emissions of industry contributed the largest share,which counts for 55%.And the emissions from the chemical industry account for 25% of the whole industry sector.(3) In the indirect emissions,the shares of emissions from cross-boundary traffic and embodied energy of key urban materials were 27% and 73% respectively.The carbon emissions of long-distance road transport take the largest proportion of cross-boundary traffic,which accounted for 38%.And the embodied energy emissions from the fuel were the most important part of embodied energy emissions,accounting for 51% of embodied emissions.(4) From the perspective of per capital carbon footprint,the per capital direct carbon emissions of Xiamen and Denver were 5.74 t CO2e/cap and 18.9 t CO2e/cap respectively.And the per capital carbon footprint of Xiamen and Denver,including direct and indirect emissions caused by the energy use,were 9.01 t CO2e/cap and 25.3 t CO2e/cap.In the total carbon footprint by Xiamen and Denver,the emissions from the cross-boundary transport all took up 10%,and the emissions caused by embodied energy of key urban materials were 26% and 15% respectively.The embodied emissions by Xiamen were relatively higher than Denver because the urbanization and industrialization consumed more materials in Xiamen.Comparing the per capital carbon emission excluding embodied emissions with typical cities in the worldwide,Denver took the first place with 21.5 t CO2e/cap,and Los Angeles,New York City,London,Bangkok,Cape Town and big city like Shanghai,Tianjin and Beijing in China were more than 10 t CO2e/cap averagely.That of Xiamen was 6.63 t CO2e/cap,which was far less than most cities.

Changing carbon footprint of urban household consumption in Beijing: Insight from a nested input-output analysis

The impacts of household structure transitions on household carbon emissions in China

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.

<p indent="0mm">With the proposal of China’s carbon neutrality target and the official entry into force of the Kigali Amendment to the Montreal Protocol, the green and efficient development of the new energy vehicle heat pump is imperative, since China has the largest number of new energy vehicles in the world. The environmental-friendly heat pump with low global warming potential (GWP) is increasingly essential for the electric vehicle (EV) to save energy consumption and extend the driving range, it is beneficial to achieve the carbon neutrality from reducing both direct and indirect carbon emissions. The long-used R134a has a great climate impact due to its high GWP, researchers have been investigating heat pump systems with low-GWP refrigerants. Previously, the life cycle climate performance (LCCP) was a widely accepted metric to evaluate the carbon footprint of mobile air conditioning systems “from cradle to grave” for the classical engine vehicle, however, the new energy vehicle heat pump has significant differences from the mobile air conditioning systems, which makes the LCCP developed for MAC systems no longer applicable for the NEV heat pump system. Such LCCP analysis about EV heat pumps can hardly be found. To facilitate the EV industry and policymakers better understand the environmental impacts of those low-GWP refrigerants, this study provides a comprehensive LCCP analysis for the EV heat pumps based on the heat pump system bench test results, local climates (six cities: Beijing, Shanghai, Guangzhou, Phoenix, Fargo, and Chicago), local power supply characteristics, real-world driving patterns, vehicle cabin thermal sensation, and climate control load. Three low-GWP refrigerants, i.e., CO₂, binary blends of CO₂ and R41 (with GWP value of 49), M2 (R410A substitute with GWP value of 137), were compared against R410A and R134a. Results show that the system COPs in the energy consumption model are estimated within ±6.5%; In most regions, the heat pump can save 36%–69% of electricity for heating on electric vehicles compared to the conventional PTC heater system; The direct emissions depend on the refrigerant GWP and are almost unaffected by the climate. The indirect emissions are influenced by the climate, refrigerant type and the carbon intensity; among the indirect emissions, manufacturing and EOL disposal account for only 5% or so, while the major part is the emissions due to system operation; The R410A heat pump shows higher levels than R134a in both direct and indirect emissions, with 11%–36% increase in total; although M2 shows 2%–27% higher indirect emissions than R134a, it reduces 90% of the direct emissions. In general, it shows 3%–35% less total emissions compared to R134a for EV heat pump application; the direct emissions of the CO₂ heat pump can be neglected while the CO₂ system can reduce 7% of the indirect emissions only in cold climates such as Fargo. Thanks to its low direct emissions, the total LCCPs of the CO₂ heat pump are reduced by 6%–27% relative to R134a except in Shanghai (+20%); among the selected refrigerants, CO₂/R41 shows the lowest LCCP, reducing 5%–42% of total emissions relative to R134a in various climates, and 1%–21% less than the CO₂ system.

PurposeThe purpose of this paper is to present a comprehensive analysis of the carbon footprint of the Delft University of Technology (TU Delft), including direct and indirect emissions from utilities, logistics and purchases, as well as a discussion about the commonly used method. Emissions are presented in three scopes (scope 1 reports direct process emissions, scope 2 reports emissions from purchased energy and scope 3 reports indirect emissions from the value chain) to identify carbon emission hotspots within the university’s operations.Design/methodology/approachThe carbon footprint was calculated using physical and monetary activity data, applying a process and economic input-output analysis.FindingsTU Delft’s total carbon footprint in 2018 is calculated at 106 ktCO2eq. About 80% are indirect (scope 3) emissions, which is in line with other studies. Emissions from Real estate and construction, Natural gas, Equipment, ICT and Facility services accounted for about 64% of the total footprint, whereas Electricity, Water and waste-related carbon emissions were negligible. These findings highlight the need to reduce universities’ supply chain emissions.Originality/valueA better understanding of carbon footprint hotspots can facilitate strategies to reduce emissions and finally achieve carbon neutrality. In contrast to other work, it is argued that using economic input-output models to calculate universities’ carbon footprints is a questionable practice, as they can provide only an initial estimation. Therefore, the development of better-suited methods is called for.

Does urbanization lead to more direct and indirect household carbon dioxide emissions? Evidence from China during 1996–2012

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.

Carbon emission has become a global focus. The construction of carbon emissions calculation model is helpful for its control. Currently, there is still no uniform method about accounting on the carbon emissions of steel products. The common calculation models are not totally suitable for China. To make up for the shortcomings of them, this paper defines the life cycle system of the iron and steel products based on EIO-LCA, measures the quantity of the direct, indirect carbon emissions and carbon emission deduction in various stages of this life cycle, identifies the hotspot and department which contributes most in carbon emission, and takes Hunan Valin Xiangtan Iron and Steel Co., Ltd (abbreviated Xiang Gang) as an example to validate it. It shows that 2103.87kg of carbon in total would be emitted when one tonne of steel is produced by Xiang Gang. Among the total, the quantity of direct, indirect and deductible carbon emission are 2033.5kg, 216.75kg and 146.38kg respectively, namely carbon emissions of producing per ton of steel is 2.1 tons. Direct carbon emissions from all stages of the life cycle of steel products mainly exist in the stage of steel production and transportation. And ferrous metal smelting and rolling processing industry are the largest emissions industries of the total indirect emissions. Converting by-product gas, heat, and pressure into electrical energy use can reduce carbon dioxide emissions by 146kg, which is the equivalent of reducing carbon dioxide emissions per ton of steel 0.15 tons. Therefore, in order to make the carbon dioxide emissions reach the advanced domestic level of 1.7 tons per ton steel, the iron and steel enterprises can meet emissions reduction targets by strengthening control of carbon emission and improving the efficiency of the utilization of secondary energy from small and large scale.

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.

This paper studies the direct and embodied carbon footprint of households’ consumption in China. The study results show that the embodied carbon footprint of Chinese households is far higher than direct carbon footprint. The direct carbon footprint of urban households is resulted from consumption of power, heat and petroleum products, while the rural households are mostly due to consumption of power and coal. The embodied carbon footprint of households’ consumption stems mostly from raw material and energy sectors with high intensities of carbon emissions, and with considerable intermediate input and demand in the production process. The per capita carbon footprint of urban households is far higher than that of rural households. The emissions reductions emphases on carbon footprint of households’ consumption are urban households, embodied carbon footprint, and the direct carbon footprint of energy consumption of rural households in China.

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

Assessment of the embodied carbon in precast concrete wall panels using a hybrid life cycle assessment approach in Malaysia

An integrated life-cycle greenhouse gas protocol accounting on oil palm trunk and empty fruit bunch biofuel production

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.