Household CO2 Research Articles

Effect of Broadband Infrastructure on Rural Household CO2 Emissions in China: A Quasi-Natural Experiment of a “Broadband Village”

This paper explores how broadband infrastructure affects rural household carbon dioxide emissions (HCE). Based on the Environmental Kuznets curve hypothesis, a quasi-natural experiment of a “Broadband Village” (B&V) in China is conducted. Panel data from 9,790 rural households were collected as part of the China Family Finance Survey (CHFS). The consumer lifestyle approach was used to calculate HCE and a Difference-in-Differences (DID) model was used to analyze the impact of a B and V pilot project on rural HCE. The results of DID model showed that B and V significantly increases rural HCE, with an influencing coefficient 1.7. Subsequently, Threshold Model was utilized to examine the nonlinear relationship between household broadband penetration and rural HCE. The results revealed the threshold effect between rural household broadband penetration and rural HCE. Namely, the growth effect of B and V to rural HCE would be much weaker, if rural household broadband penetration goes above a threshold level, 31.32%. Our analysis provides important insights for policymakers to formulate digital village and income redistribution policies to support rural carbon dioxide (CO2) emissions reductions.

Inequality in urban and rural household CO2 emissions of China between income groups and across consumption categories

There exist large differences in household CO2 emissions (HCEs) between income groups in China, and the magnitude of difference varies significantly by consumption categories and provinces. Current studies have estimated China's interregional carbon inequality, however, details of carbon inequality between income groups and across consumption categories as well as the future view are still limited. This paper uses the environmentally extended multi-regional input-output (MRIO) model linking with the household consumption survey data to calculate the HCEs, that is, the emissions embodied in goods and services across urban and rural income groups of China's 30 provinces in 2017. Carbon inequalities of HCEs are measured by Gini coefficient and the related drivers are obtained by decomposition analysis. For assessing the carbon inequality in 2030 and 2050, this paper analyzes the income elasticity of demand for various consumption categories. The results indicated that in 2017, the top 10% of the population induced 24.7% of the HCEs, while the bottom 46% of the population were responsible for 24.6% emissions. The overall carbon inequalities of China (i.e., 0.32 in 2017 and 0.41 in 2050), urban China (i.e., 0.31 in 2017 and 0.35 in 2050) and rural China (i.e., 0.27 in 2017 and 0.34 in 2050) increase until 2050 with the demand-side growth, which can be attributed to the effect of household expenditure. More in depth, the income elasticities of demand for carbon-intensive goods, such as housing, others and transport, are larger in affluent groups, which will result in higher expenditure by rich people on carbon-intensive goods with income increase so as to widening gaps of HCEs between poor and rich individuals.

Sector linkages and driving forces of Chinese household CO2 emissions based on semi-closed input-output model.

For decades, the rapid urbanization and economic development in China inevitably lead to the increase of household energy demand and CO2 emissions. Existing studies regard the household sector as an exogenous sector unrelated to production, ignoring that the wages, goods, and services provided by production sectors will affect the consumption of the household sector. This paper analyzes the impact of the household sector on production sectors and the total CO2 emissions by establishing a semi-closed environmentally extended input-output model in China from 2007 to 2017. Through the calculation of index of the power of dispersion and index of sensitivity of dispersion, the index of sensitivity of dispersion of household is large, and household has push to several sectors' CO2 emissions. Furthermore, we explore the drivers of China's household CO2 emissions using structural decomposition analysis and claim that consumption per capita and urbanization rate are the dominant factors driving household CO2 emissions, while carbon intensity and economic structure are the main factors inhibiting household CO2 emissions. Our research shows a clear picture of sector linkages and driving forces of household CO2 emissions, providing strong evidence for the development of China's household CO2 reduction policies.

Development of a Method for Estimating Household CO2 Emissions by Region: Toward the Formulation of Environmental Policies by Region

Development of a Method for Estimating Household CO2 Emissions by Region: Toward the Formulation of Environmental Policies by Region

Whose House is on Fire? Identifying Socio-Demographic and Housing Characteristics Driving Differences in the UK Household Co2 Emissions

Whose House is on Fire? Identifying Socio-Demographic and Housing Characteristics Driving Differences in the UK Household Co2 Emissions

The Characteristics and Driving Factors of Household Co2 and Non-Co2 Emissions in China

The Characteristics and Driving Factors of Household Co2 and Non-Co2 Emissions in China

Whose House is on Fire? Identifying Socio-Demographic and Housing Characteristics Driving Differences in the UK Household Co2 Emissions

Whose House is on Fire? Identifying Socio-Demographic and Housing Characteristics Driving Differences in the UK Household Co2 Emissions

Exploring household emission patterns and driving factors in Japan using machine learning methods

Given by the ambitious GHG mitigation targets set by governments worldwide, household is playing an increasingly important role for reaching listed reduction goals. Consequently, a deep understanding of its emission patterns and the corresponding driving factors are of great importance for exploring the untapped potential of household. However, how to accurately capture household emission features still demand further support from both data and method development. To bridge this knowledge gap, we try to use machine learning technology, which is well linked to the micro-level household survey data, to identify key determinants that could explain the household home-energy consumption and associated emissions. Here, we investigate the household CO2 emissions based on a representative survey which covers 31,133 households in Japan. Six types of machine learning process are employed to find key factors determining to different household emission patterns. Results show that demographic structure, average age and electricity-intensive appliances (electric water heaters, electric heaters, etc.) are most significant driving factors that explain differences in household emissions. Results also further verified that differences in driving factors can be observed in identifying various household emission patterns. The results of study provide vital information for the customized decarbonization pathways for households, as well as discussing further energy-saving behaviours from data-oriented method.

Testing the impact of first-mover advantage on household energy-related carbon emissions: an exploratory study from six urban agglomerations in China.

The household sector has become the second-largest source of energy consumption and CO2 emissions in China. It is important to understand the trends and changing mechanisms of household energy-related CO2 emissions (HECEs) in different social stages for mitigating the impact of climate change. However, the existing trends in HECEs and whether they are congruent with the expectation that HECEs in later developed areas are lower than those in priority areas under the same economic level are unclear. Here, we compared the per capita HECEs (PHECEs) of urban agglomerations under the same economic standards and analyzed the causes of the comparative results. We find that (1) HECEs increased rapidly from 3.65 × 108 t to 12.42 × 108 t during 1995-2017, with an average annual growth rate of 14.19%; (2) urban agglomerations that developed earlier do not have higher PHECEs. The PHECEs of urban agglomerations with moderate and later development do not decrease under increased social, technological, and cognitive conditions; (3) carbon intensity (CI), energy intensity (EI), and per capita regional gross domestic product (PCG) have a positive impact on PHECEs, and the potential impact of EI on PHECEs reduction is greater; (4) the effects of CI, EI, and PCG on PHECEs in different urban agglomerations are various. However, these effects are similar within urban agglomerations. Our study provides a reference for reducing CO2 emissions in the household sector and for the green development of urban agglomerations and emphasizes that the growth of HECE at a reasonable level is necessary when technology does not meet sufficient constraints.

Scenario simulations for the peak of provincial household CO2 emissions in China based on the STIRPAT model

As household CO2 emissions (HCEs) are a key source of China's CO2 emissions, exploring the mitigation potential of HCEs is significant to achieve China's 2030 emission target. However, rare literatures analyzed the future evolution of HCEs from the provincial perspective. Here, we employ the STIRPAT model and build three scenarios (i.e., baseline, low and high scenarios) to investigate the trajectories and peak times of HCEs in 30 provinces up to 2040. The results show that 25 provinces can peak HCEs before 2030 in at least one scenario, while 5 provinces cannot achieve the 2030 emission target in any scenarios. Moreover, Guangxi and Hainan will maintain growth up to 2040 in all three scenarios. At the national level, China's household sector can achieve HCEs peak in all three scenarios. Further reduction of emission intensity helps national HCEs reach the peak around 2025 in the high scenario at 1063 MtCO2. The findings suggest that Guangdong, Jiangsu, Hebei, Henan, Zhejiang and Anhui are key provinces for future HCEs reductions, because they account for more than 40% of national HCEs in 2040 in all three scenarios. Energy efficiency improvement and clean energy applications will be effective for emission reductions.

Distribution and evolutionary in household energy-related CO2 emissions (HCEs) based on Chinese north–south demarcation

Household energy-related CO2 emissions (HCEs) are significant sources of increased CO2 emissions in China. Understanding the distribution and evolution of HCEs in different geographical areas is favorable for low-carbon development and climate change mitigation. However, there has been little research on this topic, especially in geographical demarcation areas. Here, using prefecture-level cities traversed by the Qinling mountains–Huaihe river line (Qinling–Huaihe line) as the study areas, we quantified the HCEs based on statistical and nighttime light data from 2000 to 2017. We revealed the evolutionary trends and structural changes of HCEs and looked into how HCEs are impacted by social and natural factors. The results show that northern regions tend to have higher HCEs than southern regions at similar longitudes, and this trend remains stable for a long period of time due to the long-term and stable living habits and consumption behaviors with regional characteristics. Cities with the highest HCEs are located in the central region, followed by the eastern region, and the lowest in the western. The longitude evolution of HCEs is centered on medium-emission cities, which moved eastward from 111–117°E and turned major aggregation of medium-high emissions up to 2015 by the economic driving force. The distribution of fixed crosses is inconsistent with the HCEs and energy structure, demonstrating the counteracting effects of energy structure optimization on HCEs are far less than the promoting effects of energy demand. Although HCEs increased as GDP and population increased, these effects were partially offset when adjusting for urban layout and human activity density. This paper provides a reference for reducing CO2 emissions in the geographical demarcation areas and puts forward policy suggestions.

Quantifying the impacts of energy inequality on carbon emissions in China: A household-level analysis

This study empirically investigates the impact of energy inequality on household carbon dioxide (CO2) emissions in China by employing a balanced panel dataset for China's 30 provinces for the period 2000–2017. Fully considering the potential cross-sectional dependence, this study employs a series of empirical approaches allowing for cross-sectional dependence. Moreover, given the significant differences in energy inequality and household CO2 emissions, we further conduct an asymmetric analysis on the nexus between energy inequality and household CO2 emissions. The empirical results indicate energy inequality can positively affect the volume of household CO2 emissions; however, this finding makes no economic sense since it goes against the actual conditions in China (energy inequality and household CO2 emissions have shown reverse change trends in recent years). Simultaneously, we find that narrowing energy inequality can reduce the growth rate of CO2 emissions, a fact we confirm with a series of robustness tests. Notably, the impact of energy inequality on household CO2 growth is asymmetric across various quantiles (i.e., different regions). Accordingly, we highlight several relevant policy implications for the Chinese government to reduce household CO2 emissions and narrow energy inequality.

Dynamic characteristics and drivers of the regional household energy-carbon-water nexus in China.

Being a node of the energy-water consumer and carbon dioxide (CO2) emitter, the household is one key sector to pilot integrated energy-carbon-water (ECW) management. This study developed an integrated framework to explore China's provincial household ECW nexus as well as their drivers from the years 2000 through 2016. The absolute amount and growth rate of household energy use (HEU), household CO2 emissions (HCE), and household water use (HWU) were abstracted to reveal the dynamic characteristics of the household ECW nexus. Efficiency advance, income growth, urbanization, family size, and household number were defined to explain the changes in the household ECW nexus. This study revealed that there is a huge regional heterogeneity in China's household ECW nexus. Developed regions such as Zhejiang, Jiangsu, Guangdong, and Shanghai are the most important household ECW nexus nodes with larger amounts and growth rates of household ECW. Income growth overwhelmingly increases ECW, while efficiency advance effectively curbs its growth. Comparatively, household number, family size, and urbanization have small effects. Therefore, implementing differentiated management and focusing on the synergy of socioeconomic factors are the keys to achieving integrated household ECW management. And the analytical framework can be used to analyze ECW nexus from a sector, city, or country perspective.

Who emits CO2? Landscape of ecological inequalities in France from a critical perspective

This article provides an overview of the inequalities in greenhouse gas (GHG) emissions between French households. It presents in a detailed and critical manner the methodological conventions used to compute “household emissions”, and the related assumptions. The most common principle of attribution, the carbon footprint, which assigns to households the emissions of the products they consume, conveys implicit conceptions of responsibility. It focuses attention on the contributions of individuals, on their choices. It may obscure the role of non-individual actors as well as the collective component of GHG emissions, and neglect the dimensions of responsibility not related to consumption choices.We estimate the distribution of household carbon footprints based on data from the 2011 French Expenditure Survey. Household emissions tend to increase with income, but they also show a strong variability linked to geographical and technical factors that force to use fossil fuels.Based on sectoral surveys (ENTD 2008; PHEBUS 2013), we also reconstruct household CO2 emissions linked to housing and transport energy. For transport, emissions are proportional to the distances travelled due to the predominant use of private cars. Urban settlement patterns constrain both the length of daily commuting and access to less carbon-intensive modes of transport. For housing, while house sizes increase with income and distance from urban centres, the first factor to account for variability of emissions is the heating system. It has little to do with income but more to do with settlement patterns, which constrain access to the various energy carriers.Finally, we discuss the difficulties, both technical and conceptual, involved in estimating emissions of the super-rich (the top 1 percent).JEL Classification: D12, D30, Q56, R20

The Impact of Socio-economic and Environmental Sustainability on CO2 Emissions: A Novel Framework for Thirty IEA Countries

The extent to which socio-economic factors other than income and household size are associated with household CO2 emissions and whether associations vary across emission domains remains contested in the literature. We explore the impact of socio-economic and environmental sustainability indicators on CO2 emissions in the presence of combustible renewables, and the economic growth of thirty International Energy Agency (IEA) member countries. We develop a comprehensive empirical analysis using panel data and apply advanced econometric techniques for the period from 1995 to 2018. The panel co-integration analysis indicates long-run relationships among the variables. In addition, augmented mean group analysis and common correlated effects mean group analyses explain that environmental sustainability reduces CO2 emissions in the short run. Findings of fully modified least square estimates and long-run dynamic least squares estimates confirm that socio-economic sustainability increases CO2 emissions and environmental sustainability decreases them. The results of Dumitrescu and Hurlin Granger causality analysis reveal that combustible renewables, environmental sustainability, and economic growth bidirectionally Granger cause CO2 emissions, but socio-economic sustainability unidirectional Granger causes environmental quality. Policymakers in the IEA economies are encouraged to establish policies that promote a sustained lifestyle, ecological awareness, clean technological innovations, limit CO2 emissions, ecological trade-offs, and CO2 emissions ceilings to avoid rebound effects and limit environmental degradation. The study’s limitations are discussed, and useful directions for future research in the area are proposed.

Exploring Asymmetric Nexus Between CO2 Emissions, Environmental Pollution, and Household Health Expenditure in China.

ObjectiveThis study investigates the nexus between household health expenditure, CO2 emissions and environmental pollution in China. We analyzed the asymmetric dynamic relationship between CO2 emissions, environmental pollution and household health expenditure for the period 1990 to 2019 in China.MethodsThis study adopted nonlinear autoregressive distributed lag (NARDL) and Granger causality following the diagnostic test. Furthermore, we applied Dickey–Fuller (ADF), PP unit root tests, Zivot and Andrews test for structural breaks in our analysis. The NARDL is the most suitable econometric technique for estimations, especially if the asymmetric relationship exists among the variables. NARDL technique is capable to explore the dynamic relationship between CO2 emissions, environmental pollution and household health expenditure.ResultsThe empirical results verify the asymmetric nexus between CO2 emissions, environmental pollution and household health expenditure in the context of China. The outcomes revealed that in the short run and long run positive shocks of CO2 emissions and environmental pollution positively affecting health expenditure, while negative shocks reduce health spendings. The results also demonstrate bi-directional causality among household health spendings, CO2 emissions and environmental pollution.ConclusionOur results support many previous studies, documenting that CO2 emissions positively contribute to the amount of household health expenditure, confirming the asymmetric relationship between CO2 emissions and household health expenditure. The results also confirm the statistically significant and asymmetrically positive relationship between environmental pollution and household health expenditure. This implies that Chinese residents have to bear more household health expenditure, in the case of more CO2 emissions and a greater amount of environmental pollution.

Who emits CO2? Landscape of ecological inequalities in France from a critical perspective

This article provides a panorama of greenhouse gas (GHG) emission inequalities between French households. It presents in a detailed and critical manner the methodological conventions that are used to compute “household emissions”, including the related assumptions. The most common responsibility principle, the “consumer responsibility”, assigns to households the emissions of the products that they consume, resulting in the carbon footprint. It focuses attention on the contributions of individuals, on their choices, and it may obscure the role of non-individual actors and also the collective component of GHG emissions, and it neglects the dimensions of responsibility that are not related to consumption choices. We estimate the distribution of household carbon footprints based on data from the 2011 French Household Budget Survey. Household emissions tend to increase with income, but they also show a strong variability linked to geographical and technical factors that force the consumer to use fossil fuels. Based on sectoral surveys (ENTD 2008; PHEBUS 2013), we also reconstruct household CO2 emissions linked to housing and transport energy. For transport, emissions are proportional to the distance travelled due to the predominant use of private cars. Urban settlement patterns constrain both the length of daily commuting and access to less carbon-intensive modes of transport. For housing, while the size of the dwelling increases with income and distance from urban centres, the first factor to account for variability of emissions is the heating system: this has little to do with income but more to do with settlement patterns, which constrain access to the various energy carriers. Finally, we discuss the difficulties, both technical and conceptual, that are involved in estimating emissions from the super-rich (the top 1 percent).

Drivers of household decarbonization: Decoupling and decomposition analysis

Abstract With a significant spike in consumption in China, direct household CO2 emissions (HCEs) have increased sharply; hence, exploring the existence and drivers of decarbonization in the household sector is essential. Using the decoupling method, we explored the decarbonization trend against variations in per capita consumption across urban and rural areas at both national and provincial levels. Thereafter, we detected the factors driving household decarbonization between 2005 and 2015 through a decomposition analysis. A decarbonization trend (expansive negative decoupling to weak decoupling) was observed in urban areas at the national level; in particular, strong decoupling was observed during 2007–2008. However, a decarbonization trend was indiscernible for rural areas. At the provincial-level, a significant decarbonization trend (weak decoupling to strong decoupling) mainly existed for urban areas across heavily industrialized and renewables-based provinces, and also for rural areas across developed provinces. Energy intensity and per capita consumption primarily drove household decarbonization, accounting for over 50% of the total change in HCEs at the national-level. Moreover, the fossil fuel mix and fossil fuel share showed significant negative influences on an increase in HCEs, specifically for heavily industrialized and renewables-based provinces.

Does income inequality affect direct and indirect household CO2 emissions? A quantile regression approach

Income inequality plays as a driver of direct and indirect CO2 emissions from the household sector. Existing research has done a lot of work on the accounting methods and influencing factors of household direct CO2 emissions and indirect CO2 emissions. There are few studies on the impact of income inequality on direct and indirect household CO2 emissions under different emission levels. This research investigates the impact of income inequality on both direct and indirect CO2 emissions of 30 provinces in China from 2000 to 2015 using the STIRPAT model and panel quantile regression method. Theil index is selected to measure income inequality, population, per capita consumption level, energy intensity, urbanization, and industrial structure as control variables. The results indicate that the level of regional inequality is gradually increasing from the east to the west. The household indirect CO2 emissions in the east increased fastest, while the household direct CO2 emissions in the west increased fastest. Income inequality significantly promoted direct CO2 emissions under all quantile levels, while the impact on indirect CO2 emissions is not significant. Population and per capita consumption will promote direct and indirect CO2 emissions. Urbanization has no significant impact on direct CO2 emissions. Only at the 90th percentile level is there a significant negative correlation between energy intensity and indirect CO2 emissions. Industrial structure significantly increases direct CO2 emissions at the 50th, 75th, and 90th percentiles, while the impact on indirect CO2 emissions is not significant.

Hourly Firewood Consumption Patterns and CO2 Emission Patterns in Rural Households of Nepal

Nepal has low per-capita energy use and a majority of its rural residents use firewood as their primary energy source. Excessive use of firewood in improperly ventilated buildings degrades the indoor environment and health condition of the inhabitants. This study aims to assess the influence of hourly firewood consumption patterns on CO2 emissions and resulting concentrations in rural households in Nepal. A field survey was conducted for 24 h in 16 households during winter. The results suggest that most of the households tend to use more firewood during the morning and evening hours. Family size and number of animals reared by the households were positively correlated with firewood consumption, whereas family size was negatively correlated with per-capita firewood consumption. Per-capita firewood consumption was found to be 1.8 kg/(capita·day). Household firewood consumption and CO2 emissions were 12 kg/(family·day) and 14.26 kg CO2 e/(household·day), respectively. The larger households spent more time for cooking, while their consumption rate was similar (1.3 kg/h) to that of smaller households. High indoor CO2 emissions in the morning and evening hours due to high firewood consumption may pose severe health risks to the inhabitants. Therefore, intensive awareness programs and pollution control programs are suggested for improving indoor air quality and health condition of the rural population.