Indirect Carbon Emissions Research Articles

Spatio-temporal interaction heterogeneity and driving factors of carbon emissions from theconstruction industry in China.

Global warming caused by carbon emissions has become a major issue that countries need to address. As the largest carbon emitter globally, the construction industry is one of the major contributors to carbon emissions in China. It is of significance for carbon reduction to study carbon emission from construction industry. Based on various methods, this study explored the spatio-temporal characteristics of carbon emissions and the driving factors of construction industry. This study found, in 2007, 2010, and 2012, carbon emissions from the construction industry exhibited an increasing trend, and the indirect carbon emissions accounted for approximately 77% of the total carbon emissions overall; in addition, the regional gaps in carbon emissions are widening. The space centers of gravity of direct, indirect, and total carbon emissions showed similar rotations in the counterclockwise direction and gradually shifted to the northeast direction. Carbon emissions from the construction industry were predominantly influenced by the total population, number of employees in construction industry, labor productivity in construction industry, added value of the construction industry, energy consumption in construction industry in 2007, evolution to the mutual influence of the total population, labor productivity in construction industry, and energy consumption in construction industry in 2012. The finds can make references for the regional sustainable development.

Quantifying universities’ direct and indirect carbon emissions – the case of Delft University of Technology

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.

Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

The indirect carbon emission from electrical consumption of wastewater treatment plants (WWTPs) accounts for large proportions of their total carbon emissions, which deserves intensive attention. This work proposed an automated machine learning (AutoML)-based indirect carbon emission analysis (ACIA) approach and predicted the specific indirect carbon emission from electrical consumption (SEe; kg CO2/m3) successfully in nine full-scale WWTPs (W1–W9) with different treatment configurations based on the historical operational data. The stacked ensemble models generated by the AutoML accurately predicted the SEe (mean absolute error = 0.02232–0.02352, R2 = 0.65107–0.67509). Then, the variable importance and Shapley additive explanations (SHAP) summary plots qualitatively revealed that the influent volume and the types of secondary and tertiary treatment processes were the most important variables associated with SEe prediction. The interpretation results of partial dependence and individual conditional expectation further verified quantitative relationships between input variables and SEe. Also, low energy efficiency with high indirect carbon emission of WWTPs was distinguished. Compared with traditional carbon emission analysis and prediction methods, the ACIA method could accurately evaluate and predict SEe of WWTPs with different treatment scales and processes with easily available variables and reveal qualitative and quantitative relationships inside datasets simultaneously, which is a powerful tool to benefit the “carbon neutrality” of WWTPs.

Impact of population ageing on carbon emissions: A case of China's urban households

Ageing can have a considerable ripple effect on carbon emission by affecting production and consumption of economic system. Based on an input-output structural decomposition analysis, this study analyses the impact of ageing on urban residents’ carbon emissions in China from 2002 to 2007 and 2007 to 2012, and explores the regional heterogeneity and temporal uncertainty of the impact. At the national level, older adults slowed the growth of direct carbon emissions in both periods, because their per capita energy consumption fell more in the first period, then switched from coal to gas and electricity consumption. They contributed less to the growth of indirect carbon emissions in both periods due to the minimum increase in their per capita consumption among all age groups. Moreover, the impact of ageing on carbon emissions had clear regional heterogeneity. This study is beneficial to provide suggestions regarding carbon emission reduction and adapting to demographic changes.Ageing; Household consumption; Carbon emissions; Input-output; Structural decomposition analysis.

An improved carbon emission flow method for the power grid with prosumers

Nowadays, with the wide installation of distributed energy resources and independent energy storage systems, prosumers as a new type of electricity market entity have emerged. Since numerous prosumers can significantly impact the carbon emission of the power grid, this paper proposes an improved carbon emission flow method for the power grid with prosumers. This method can accurately clarify the detailed distribution of electrical carbon emission flow in power grids. First, based on the power flow, prosumers’ impacts on the electrical carbon emission are quantified from three aspects that include the carbon emission sources, the network flow, and the indirect carbon emission individuals. Then, an improved power carbon emission flow model is proposed, in which the complex carbon emission intensity of prosumers is derived emphatically. Finally, case studies based on the IEEE 30-bus system verify the feasibility of the proposed method. This method provides a measurement basis for further research considering electrical carbon emissions.

A heat integration case study for the dehydration and condensate stabilization units in LNG plants for economic and energy savings

Liquefied Natural Gas, derived from natural gas is the cleanest fossil fuel, and it plays a critical role in global energy markets, although its production is costly. To partially reduce the cost, this study considers the hot section (pre-separation) of LNG process and integrates the Tri-Ethylene Glycol Dehydration and Condensate stabilization units for maximum energy savings using the Pinch Analysis technique, which is conducted through Aspen Energy Analyser V10. Plant simulations are used to verify the feasibility and functionality of the whole heat integration design. The energy analysis reveals ten heat exchange networks (HEN) comprising of two heaters and five coolers and one process heat exchanger for the base case simulation. Whereas the total energy required for the base case is 80.86 MW and indirect carbon emissions are 395.3 tonne/d. After integrating the units, the energy consumption for heating and cooling are reduced by 18.83% and 21.37%, respectively, while carbon emissions are reduced by 316.2 tonne/d.

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

Improper discard of oil palm trunk and empty fruit bunch renders massive greenhouse gases. Turning these palm wastes into solid biofuels could aid in carbon reduction. The embodied environmental impacts of the solid biofuel densification process are neglected in carbon emission quantification studies applying Greenhouse Gas Protocol while the significance of classifying the system's direct and indirect carbon emissions were overlooked in those utilising life cycle assessment. Despite the prospect of both methodologies to complement their limitations for carbon emissions quantification, no study integrates both methodologies to investigate direct and indirect emissions systematically from a life cycle perspective. An integrated framework of life cycle assessment and Greenhouse Gas Protocol is developed to quantify the direct and indirect carbon emissions of oil palm trunk and empty fruit bunch densification from cradle-to-gate for three pellet plants in Indonesia and Malaysia. The emissions are categorised into three emission scopes: Scope 1, Scope 2, and Scope 3 according to the Greenhouse Gas Protocol, integrated with avoided emissions which are quantified via life cycle assessment. The pellet plants generate 534.7–732.3 kg CO2-eq/tonnepellet per hour, in which Scope 1 (i.e., direct emissions) is the major emission scope due to high emissions from wastewater production and drying fuel combustion. Washing equipment (169.2–439.0 kg CO2-eq/tonnepellet per hour) and burners (87.1–214.5 kg CO2-eq/tonnepellet per hour) are the hotspots found in the pellet plants. Producing empty fruit bunch pellets could reduce 62.0–74.1 % of emissions than landfilling the empty fruit bunch. Empty fruit bunch pellet and oil palm trunk pellet are recommended to co-fire with coal to phase down coal usage in achieving COP26 pledge. This study provides data-driven insights for quantifying carbon emissions through the integrated framework and could be a reference in future life cycle carbon footprint studies of the biomass densification process.

How population migration affects carbon emissions in China: Factual and counterfactual scenario analysis

China's large-scale inter-provincial population migration has a vast impact on social changes, and significantly altered the energy consumption demand of various provinces, and further affected the spatial distribution of carbon emissions. Nevertheless, less attention has been paid to the impact of population migration on carbon emissions. To make up for the niche, this paper develops an innovative framework by integrating a geographically weighted regression model, a population migration matrix, and an environmentally extended multi-regional input-output model to assess the impact quantitatively based on factual and counterfactual scenarios. The results show that the carbon emissions increased by 78.16 million tonnes (Mt) in 2017 due to population migration, of which direct carbon emissions increased by 6.91 Mt. and indirect carbon emissions increased by 71.25 Mt. It must be emphasized that population migration increases indirect carbon emissions driven by urban consumption and investment in the provinces with net in-migrants. But these provinces transfer more carbon emissions to the regions with net out-migrants, which implies that population migration exacerbates carbon emissions inequality. This especially increases the carbon emission reduction barrier of provinces with net out-migrants. Overall, these empirical discoveries shed light on making regionally coordinated carbon reduction policies.

Life cycle climate performance (LCCP) evaluation model for electric vehicle heat pumps and emission reduction analysis of low-GWP refrigerants

<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.

Smart cities and urban household carbon emissions: A perspective on smart city development policy in China

Smart cities have the potential to achieve the goal of carbon neutrality, but empirical evidence regarding the effect of smart city policies on carbon emissions is limited. In this novel study, we investigate the effect of China's smart city policy on household daily consumption and carbon emissions. The empirical analysis is based on a difference-in-differences regression, in which the dependent variable is the household indirect carbon emissions estimated based on the consumer lifestyle approach and a dataset of 201 Chinese cities between 2010 and 2018. The main findings are as follows. First, the smart city policy significantly reduces household indirect carbon emissions, and the reduction is greater when the policy pilot region includes the whole city rather than only certain subareas. Second, the mechanisms through which the smart city influences household indirect carbon emissions are mainly the reductions in transportation- and education-related emissions resulting from developments in smart technology. Third, the above effects last for years, and they are regionally heterogeneous and more significant in the cities of eastern China. The findings in this paper suggest that smart cities can potentially achieve city sustainability by promoting greener consumption behavior.

Tracking the CO2 Emissions of China’s Coal Production via Global Supply Chains

Coal’s green mining and scientific utilization is the key to achieve the national vision of carbon peak by 2030 and carbon neutrality by 2060. Clarifying the CO2 flow of coal production is the core part of decarbonization. This study uses an environmental extended multi-regional input–output (EEMRIO) model to analyze the impact of embodied emissions on the indirect CO2 emission intensity of coal production between China’s coal mining sector and 141 countries/regions. It is found that the CO2 emission intensity of China’s coal production was 34.14 gCO2/MJ in 2014, while the direct and indirect emission intensities were 16.22 gCO2/MJ and 17.92 gCO2/MJ, respectively. From 2007 to 2014, the direct emission intensity of China’s coal production increased by 23%, while the indirect emission intensity decreased by 30%. The key material and service inputs affecting indirect carbon emissions of coal production in China are electricity service, metal manufacturing, chemical products, coal mining, and transport, which accounted for 85.5% of the total indirect emission intensity of coal production in 2014. Globally, a large portion of CO2 from Chinese coal production is emitted to meet foreign direct and indirect demands for material and service inputs. Policy implications related to this outcome are further discussed in the study.

Integrated Process for Producing Glycolic Acid from Carbon Dioxide Capture Coupling Green Hydrogen

A novel process path is proposed to produce glycolic acid (GA) from CO2 as the feedstock, including CO2 capture, power-to-hydrogen, CO2 hydrogenation to methanol, methanol oxidation to formaldehyde, and formaldehyde carbonylation units. The bottlenecks are discussed from the perspectives of carbon utilization, CO2 emissions, total site energy integration, and techno-economic analysis. The carbon utilization ratio of the process is 82.5%, and the CO2 capture unit has the largest percentage of discharge in carbon utilization. Among the indirect emissions of each unit, the CO2 hydrogenation to methanol has the largest proportion of indirect carbon emissions, followed by the formaldehyde carbonylation to glycolic acid and the CO2 capture. After total site energy integration, the utility consumption is 1102.89 MW for cold utility, 409.67 MW for heat utility, and 45.98 MW for power. The CO2 hydrogenation to methanol makes the largest contribution to utility consumption due to the multi-stage compression of raw hydrogen and the distillation of crude methanol. The unit production cost is 834.75 $/t-GA; CO2 hydrogenation to methanol accounts for the largest proportion, at 70.8% of the total production cost. The total production cost of the unit depends on the price of hydrogen due to the currently high renewable energy cost. This study focuses on the capture and conversion of CO2 emitted from coal-fired power plants, which provides a path to a feasible low-carbon and clean use of CO2 resources.

The exploration of joint carbon mitigation actions between demand- and supply-side for specific household consumption behaviors — A case study in China

The design of “fairness” and “justice” climate policies for controlling indirect household carbon emissions (IHCEs) requires joint efforts from the demand side and supply side. On the demand side, this study constructs behavior-specific IHCEs inventory for different household groups (differed by income level) in different regions to identify high-emitting households and corresponding high-emitting consumption behaviors. On the supply side, the “Carbon Account” is set up for different regions regarding various household consumption behaviors to screen all potential emitting-related stakeholders in the entire demand–supply chains. Using 25 Chinese provinces as study cases, the findings indicate that carbon inequalities across household groups are greater than income inequalities in poorer provinces, whereas the opposite is true in richer provinces. For most high emitting households (most were middle-high- and high-income households) in 25 provinces, Food & Beverage, Housing, and Durable goods are the top 3 carbon-intensive consumption behaviors, together accounting for 64–81% (72% on average) of total IHCEs. For the three carbon-intensive behaviors, the province-level carbon account analysis shows that a province's self-production emissions (local consumption, local emissions) took up an average of 52%, 70%, and 47% of its carbon accounts. An average of 83%, 80%, and 79% of net carbon transfer-in of a province (non-local consumption, local emissions) sourced from its 5 major consuming provinces, while 76%, 81%, and 77% of transfer-out (local consumption, non-local emissions) of a province were outsourced to its 5 major producing provinces, respectively. Furthermore, according to the sector-level carbon accounts, an average of 75%, 89%, and 76% of the sector carbon transfer-in, and an average of 72%, 80%, and 79% of the sector carbon transfer-out of a province were emitted by three dominant local or non-local industrial sectors, respectively. This study could provide policy implications for local governments to take joint climate mitigation strategies between the demand-side and supply-side targeting high-emitting household consumption behaviors.

How does sponge city construction affect carbon emission from integrated urban drainage system?

The integrated urban drainage system has complicated sources of greenhouse gases (GHG) emissions, which is one of contribute to climate change. As a new model of low-carbon ecological city development in China, sponge city has great potential of carbon emission reduction. However, limited by the lack of awareness of the importance of carbon emissions from integrated urban drainage system, few studies have focused on the impact of sponge city construction on the carbon emissions from integrated urban drainage system. This study proposed an accounting framework for carbon emissions from integrated urban drainage system. The emission factor method was used to calculate carbon emissions. A case study was conducted in Xiamen, China, the carbon emission level of integrated urban drainage system and the carbon emission reduction effect of sponge city construction were discussed. The results show that the carbon emission from traditional city drainage system and sponge city drainage system accounts for about 3.4% and 1.7% of the city's carbon emission, respectively. Wastewater treatment and Sludge Treatment and Disposal are the main contributors to the carbon emissions from integrated urban drainage system. Electricity consumption is the main contributors to the indirect carbon emissions. Adjusting the sludge disposal method has the best carbon emission reduction effect, raising the standard of wastewater treatment plant tail water will lead to an increase in carbon emissions. Compared with the traditional city drainage system, the sponge city drainage system can reduce carbon emissions from integrated urban drainage system by an average of 49%, and the corresponding economic benefit was approximately 91–297 CNY/104 m3 wastewater. This study plays a positive role in understanding the carbon emission level of integrated urban drainage system and planning the low-carbon development of the city in the future.

Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis

Household consumption has become an important field of carbon dioxide emissions. Urban–rural disparity in the household carbon emissions (HCEs) of residents and their influencing factors are relevant to HCE reduction. Taking Fujian as the study area, the LMDI and SDA models were used to analyze the effects of influencing factors for the direct household carbon emissions (DHCEs) and indirect carbon emissions (IHCEs) of urban and rural residents from 2006 to 2018. The HCEs continue to rise, approximately 65% from the IHCEs in 2017, and urban areas occupied 67% in 2018. The gap between urban and rural per capita HCEs is narrowing. In 2017, approximately 75% of urban per capita HCEs came from the IHCEs, while the per capita DHCEs’ occupation exceeded the IHCEs in rural areas. Per capita consumption expenditure has the largest positive effect on the DHCEs and IHCEs in urban and rural areas. With the urbanization process, the inhibition effect of rural DHCEs is larger than the positive effect of the urban DHCEs, while the positive impact on urban areas is more substantial than on rural areas in the IHCEs. Combined with regional differences, urban and rural areas should take “common but differentiated” emission reduction responsibilities.

Underestimated environmental benefits of tailings resource utilization: Evidence from a life cycle perspective

Mineral resources are an essential foundation for economic and social development. However, many tailings generated and stockpiled during the mining process have become an obstacle to developing a low-carbon and green economy. In this paper, we selected copper tailings as a representative case study. First, we used material flow analysis for copper tailings in Tongling City and then constructed a life cycle assessment model. Finally, we set up a detailed scenario analysis of the carbon reduction benefits of copper tailings resource utilization. The results show that (1) the current comprehensive utilization of tailings is relatively single, with low added value, a common problem in the tailings resource utilization industry. (2) The resource utilization of tailings has positive environmental benefits. The global warming potential (GWP) is more prominent (e.g., preparation of cement and autoclaved blocks can achieve carbon emission reduction benefits of about 200 kgCO2/t tailings). (3) In a scenario in which the historically stockpiled tailings can be used rationally, the indirect carbon emission reduction is comparable to the carbon emission of power generation in Tongling City in one year. The resource utilization of tailings will relieve the pressure on resources and contribute to the realization of China's ‘Carbon Peaking and Neutrality Goals,’ with a significant benefit of emission reduction. At present, there are few such studies on bulk industrial solid waste. The research framework in this paper can provide reference for environmental impact assessment of other types of bulk industrial solid waste and provide policy guidance for industrial layout and development planning in similar resource-based and industrial cities and even at a larger scale.

China's carbon inequality of households: Perspectives of the aging society and urban-rural gaps

To take the carbon reduction responsibility into consumption activities and reveal the discrepancy of carbon footprint (CF) among different types of households, this study investigates CF and inequality of household consumption in 25 provinces in China by calculating combing eight categories covering both direct and indirect household carbon emissions. Importantly, the aging society and urban-rural gaps are considered. The findings indicate that per capita CF varies appreciably among the different household groups, and the aged tend to generate less emission. However, the CF of a young person may not decrease as he/she ages owing to completely diverse expenditure preferences from current older people. We revealed that people living in urban regions have larger CF than those in rural regions, and the urban–rural gap promotes inequality in household CF. Furthermore, there is a correlation between the aging of population and urban-rural gaps. The young people migrate from villages to cities, leaving the elderly and children at villages. Therefore, the aging of population in rural regions is worse than that in cities, which raises the carbon inequality among households.

Systems Accounting for Carbon Emissions by Hydropower Plant

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.

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach.

Eco-efficiency analysis can provide useful information about sustainability in the tourism industry, which has an important role in both global economy recovery and Sustainable Development Goals (SDGs), generating considerable indirect carbon emissions with respect to the supply chain due to its significant connections to other industries. This study, from the perspective of tourism sectors, including tourism hotels, travel agencies, and scenic spots, integrated the environmentally extended input–output analysis (EEIO) and data envelopment analysis (DEA) models to develop a research framework, analyzing the indirect carbon emissions of the tourism supply chain, evaluating eco-efficiency with respect to both direct carbon emissions and total carbon emissions (including direct and indirect parts), and exploring the driving factors of eco-efficiency of tourism sectors using Tobit regression models. This study took Gansu as a case, a province in China characterized by higher carbon intensity, an underdeveloped economy, and rapid tourism growth. The results demonstrate that (1) tourism hotels contribute the most carbon emissions in tourism sectors, especially indirectly due to the supply chain, with carbon emissions mainly resulting from the manufacturing of food and tobacco; (2) the eco-efficiency of tourism sectors in Gansu presents a U-shaped curve, which is consistent with Kuznets’ theory; and (3) energy technology is key to improving the eco-efficiency of tourism sectors. The research results provide a clear path for the reduction of carbon emissions and the improvement of eco-efficiency in Gansu tourism sectors. Against the backdrop of global climate change and the post-COVID-19 era, our research framework and findings provide a reference for similar regions and countries who are in urgent need of rapid tourism development to effect economic recovery.

Estimation and Influencing Factor Analysis of Carbon Emissions From the Entire Production Cycle for Household consumption: Evidence From the Urban Communities in Beijing, 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.