Spatial differentiation of carbon emissions from residential energy consumption: A case study in Kaifeng, China

Causal relationship between household consumption transition and CO2 emission in China: a dynamic panel model

The mitigation of carbon dioxide (CO2) generated from household consumption, accounting for 52% of China's total greenhouse gas emissions, plays a pivotal role in China's pursuit of reaching a carbon peak by 2030. The study used three waves of nationally representative longitudinal data, energy statistics data, and input-output table to estimate household CO2 emissions (HCEs) in China at the micro-scale. The dynamic relationship between household consumption pattern transition and HCEs per capita was explored by applying maximum likelihood and structural equation modeling (ML-SEM) with panel data. The results indicate that per capita HCE level in a given year appears to be positively associated with HCE level for the same household in the previous year. A U-shaped relationship between consumption pattern transition and HCEs per capita was confirmed, as well as the reinforcement effect of income on the impacts of consumption pattern transition. The increase in consumption propensity, household income, share of wage-income, household asset values, and house space results in higher HCEs per capita. The family size and dependency ratio have a negative relationship with HCEs, whereas households that are female-oriented and more Internet-dependent tend to produce more CO2. Exploring the consumption transition of households is crucial for reducing CO2 emissions at the household level in China.

Driving impact and spatial effect of the digital economy development on carbon emissions in typical cities: a case study of Zhejiang, China

The digital economy (DE) not only drives economic innovation and development but also has significant environmental effects by promoting lower carbon emissions. To investigate the spatial effects of DE on urban carbon emissions, this study comprehensively measures the level of DE development based on the panel data from 11 typical cities in Zhejiang Province from 2011 to 2020, by comparing analysis using different regression models. The following conclusions are obtained: (1) The total carbon emissions (TC) of Zhejiang cities in general show a fluctuating change trend of first increasing and then slowly decreasing, while carbon emission intensity and carbon emission per capita in general show a fluctuating change trend of decreasing. Cities with high TC are primarily concentrated in the Hangzhou Bay city cluster, accounted for 62 ~ 65% of the province's carbon emissions. The development of the DE in Zhejiang cities shows steady growth, but there are large differences among cities, with Hangzhou and Ningbo standing out as particularly prominent. (2) There is a significant inverted U-shaped relationship between the DE and the level of carbon emissions in Zhejiang Province. The influence coefficient of the DE on the primary term of TC is 0.613, and the influence coefficient of the quadratic term of TC is - 1.008. (3) In terms of the spatial spillover effect of the DE on carbon emissions, the study finds that compared to the direct effect, the spatial spillover effect is not significant. However, the allocation of transport resources shows a positive spatial spillover effect (increasing carbon emissions, coefficient value is 0.138), while technological progress shows a somewhat negative spatial spillover effect (decreasing carbon emissions, coefficient value is - 0.035). (4) The study also finds that the smart city pilot policy significantly reduces urban carbon emissions. Moreover, the effect of the DE on carbon emissions is confirmed through the significance test of the quadratic term when replacing the geographical and economic distance weight matrices. This indicates that the empirical findings are robust to these tests. Finally, several countermeasures to reduce carbon emissions are proposed from the perspective of DE development.

Effects of land-use change on carbon emission and its driving factors in Shaanxi Province from 2000 to 2020

Exploring the process of carbon emissions under the "carbon peaking and carbon neutrality goals" can contribute to sustainable economic development. This research takes Shaanxi Province as an example. We elaborated on the spatial and temporal characteristics of land-use change from 2000 to 2020 and adopted the carbon emission model method to calculate land-use carbon emissions, also used urban morphological indicators to reveal the main factors of carbon emission changes. The results show that from 2000 to 2020, the land-use change in Shaanxi Province is mainly reflected in the increase in construction land area and the decrease in agricultural land area. Among them, the construction land area increased by 2192 km², and the agricultural land area decreased by 5006 km². Land-use carbon emissions increased by 1.28 × 10¹¹ kg during this period. Construction land is a major contributor to carbon emissions. The forestland is the main carbon sink. Carbon emissions showed a spatial pattern of "high in the north, low in the south, and concentrated in the middle." Urban form change is the driving factor affecting land-use carbon emissions in Shaanxi Province. The results of the research contribute to the understanding of regional carbon emission mechanisms and provide a scientific basis for reducing carbon emissions.

Regional differences and driving factors of carbon emission intensity in China's electricity generation sector

As an industry with immense decarbonization potential, the low-carbon transformation of the power sector is crucial to China's carbon emission (CE) reduction commitment. Based on panel data of 30 provinces in China from 2000 to 2019, this research calculates and analyzes the provincial CE intensity in electricity generation (CEIE) and its spatial distribution characteristics. Additionally, the GTWR model based on the construction explains the regional heterogeneity and dynamic development trend of each driving factor's influence on CEIE from time and space. The main results are as follows: CEIE showed a gradual downward trend in time and a spatial distribution pattern of high in the northeast and low in the southwest. The contribution of driving factors to CEIE has regional differences, and the power structure contributes most to the CEIE of the power sector, which promotes regional CE. Concurrently, most provinces with similar economic development, technological level, geographic location, or resource endowment characteristics show similar spatial and temporal trends. These detections will furnish broader insights into implementing CE reduction policies for the regional power sector.

Measurement of provincial carbon emission efficiency and analysis of influencing factors in China

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies.

Development of an extended STIRPAT model to assess the driving factors of household carbon dioxide emissions in China

Although the past twenty years have witnessed China's remarkable economic development, the cost in terms of greenhouse gas emissions and a deteriorating environment has been enormous. Numerous studies have revealed the influence of household factors on household carbon dioxide emissions (HCEs) and called for a reduction of HCEs to mitigate climate change, but few have focused on assessing the most significant household driving factors of HCEs. Using statistical data between 2005 and 2019 in Jiangsu, China, this study developed an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model to assess the most significant driving factors of HCEs. The results show that the most significant driving factors are household size, total population, unemployment, and urbanisation rate. The study found that HCEs are positively impacted by household size while negatively impacted by the unemployment rate. Based on the study's findings, the following suggestions are proposed to lower HCEs: (i) establish an optimal consumption concept to guide residents towards consuming reasonably; (ii) cultivate a low-carbon concept among residents and promote low-carbon emissions living; and (iii) pay close attention to population structure factors and formulate effective measures accordingly. The study provides insightful information on the key driving factors of HCEs, which can facilitate achieving carbon emissions neutrality.

Spatial and temporal evolution characteristics and spillover effects of China's regional carbon emissions

China's carbon emissions account for approximately a quarter of the world's total greenhouse gas emissions. In 2020, China's fossil fuels accounted for approximately 85% of the primary energy demand, with coal alone accounting for 60%. Considering the severe global warming situation, it is necessary to reveal the spatial and temporal differences and analyze the spillover effects of carbon emissions between regions. In this study, a positive and significant spatial correlation between regional carbon emissions in China was found using an exploratory spatial data analysis. The spatial Durbin model was then utilized to explore the direct and spillover effects of factors that included economic growth, the energy intensity, and the level of technological innovation on regional carbon emissions. Whether a direct effect or a spillover effect, economic growth and improvements in the regional levels of technological innovation had significant inhibitory effects on carbon emissions both in the long term and in the short term. Specifically, an increase of 1% in the level of technological innovation led to a reduction of approximately 0.17% in the region's carbon emissions. However, a growth in the energy intensity will increase carbon emissions. In addition, an increase in the technological input intensity will lead to an increase in carbon emissions in local regions. However, an increase in neighboring regions will restrain carbon emissions in a local region. Based on these findings, it is recommended that the government accelerate regional innovation synergies and increase investment in clean energy technologies.

Influencing Factors of Direct Carbon Emissions of Households in Urban Villages in Guangzhou, China

China's household energy consumption has obvious regional differences, and rising income levels and urbanization have changed the ability of households to make energy consumption choices. In this paper, we analyze the energy consumption characteristics of urban village residents based on microlevel household survey data from urban villages in Guangzhou, China. Then, the results of modeling the material flows of per capita carbon emissions show the most dominant type of energy consumption. OLS is applied to analyze the influencing factors of carbon emissions. We find that the per capita household carbon emissions in urban villages are 722.7 kg/household.year, and the average household carbon emissions are 2820.57 kg/household.year. We also find that household characteristics, household size, household appliance numbers, and carbon emissions have a significant positive correlation, while income has no significant effect on carbon emissions. What is more, the size and age of the house have a positive impact on carbon emissions. Otherwise, the new finding is the demonstration that income is not significantly correlated with household carbon emissions, which is consistent with the characteristics of urban villages described earlier. On the basis of this study, we propose more specific recommendations regarding household energy carbon emissions in urban villages.

Spatiotemporal variations and structural characteristics of carbon emissions at the county scale: a case study of Wu'an City

In China, the county is not only an important component of industrial areas and a large contributor of carbon emissions, but also a key administrative unit for the implementation of carbon peak and carbon neutrality goals and policies. The spatiotemporal variations and structural characteristics of carbon emissions at the county scale are of great significance to China's dual goals of regional carbon policy implementation and low carbon spatial planning. Thus, it is important and insightful to conduct an in-depth and detailed examination of these characteristics while focusing on a typical iron and steel industry county-level city in North China. This study systematically calculated the carbon emissions of the county-level city of Wu'an from 2008 to 2017, and explored their structural characteristics and spatiotemporal variations. The results showed that (1) under the influence of macroeconomic and national policies, the carbon emissions of county-level cities dominated by the iron and steel industry show obvious phased characteristics; (2) there is a significant negative correlation between industry carbon emission concentrations and industrial carbon emissions; (3) within the steel industry system, sintering, iron smelting, steelmaking, and metal product processing are the main sources of carbon emissions, and the coal-based production process of the iron and steel industry needs a fundamental reformation; and (4) the carbon emission of Wu'an City shows obvious spatial differentiation characteristics. The geographic distribution of carbon emissions in Wu'an City is very unbalanced and tended to cluster together in urban areas, industrial and mining areas, and major towns. Taking 2014 as the turning point, the spatial pattern of carbon emissions in Wu'an City presents different variation characteristics.

Carbon Emissions in the Yellow River Basin: Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors Based on a Logarithmic Mean Divisia Index (LMDI) Decomposition Method

The “14th Five-Year Plan” period is a critical period and a window to obtain emission peak and carbon neutrality in China. The Yellow River Basin, a vital location for population activities and economic growth, is significant to China’s emission peak by 2030. Analyzing carbon emissions patterns and decomposing the influencing factors can provide theoretical support for reducing carbon emissions. Based on the energy consumption data from 2000–2019, the method recommended by Intergovernmental Panel on Climate Change (IPCC) is used to calculate the carbon emissions in the Yellow River Basin. The Logarithmic Mean Divisia Index (LMDI) decomposition method decomposes the influence degree of each influencing factor. The conclusions are as follows: First, The Yellow River Basin has not yet reached the peak of carbon emissions. Regional carbon emissions trends are different. Second, Shandong, Shanxi, Henan and Inner Mongolia consistently ranked in the top four in total carbon emissions, with low carbon emission efficiency. Third, Economic development has the most significant contribution to carbon emissions; other factors have various effects on nine provinces.

Spatiotemporal Dynamics of Direct Carbon Emission and Policy Implication of Energy Transition for China’s Residential Consumption Sector by the Methods of Social Network Analysis and Geographically Weighted Regression

As China’s second largest energy-use sector, residential consumption has a great potential for carbon dioxide (CO2) reduction and energy saving or transition. Thus, here, using the methods of social network analysis (SNA) and geographically weighted regression (GWR), we investigated the spatiotemporal evolution characteristics of China’s residential CO2 emissions (RCEs) from direct energy use and proposed some policy suggestions for regional energy transition. (1) From 2000 to 2019, the total direct RCEs rose from 396.32 Mt to 1411.69 Mt; the consumption of electricity and coal were the primary sources. Controlling coal consumption and increasing the proportion of electricity generated from renewable energy should be the effective way of energy transition. (2) The spatial associations of direct RCEs show an obvious spatial network structure and the number of associations is increasing. Provinces with a higher level of economic development (Beijing, Shanghai, and Jiangsu) were at the center of the network and classified as the net beneficiary cluster in 2019. These provinces should be the priority areas of energy transition. (3) The net spillover cluster (Yunnan, Shanxi, Xinjiang, Gansu, Qinghai, Guizhou) is an important area to develop clean energy. People in this cluster should be encouraged to use more renewable energy. (4) GDP and per capita energy consumption had a significant positive influence on the growth of direct RCEs. Therefore, the national economy should grow healthily and sustainably to provide a favorable economic environment for energy transition. Meanwhile, residential consumption patterns should be greener to promote the use of clean energy.

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.

Carbon emissions' spatial-temporal heterogeneity and identification from rural energy consumption in China

Carbon emissions from industry and cities have been the focus of global carbon emissions control, but the need to reduce carbon emissions from large agricultural countries cannot be ignored. This study measured rural carbon emissions based on the energy consumption of rural residents and agricultural production from 2000 to 2018 in China, and the spatial-temporal evolution and variation of rural carbon emissions were analyzed using the quadrant diagram method and Theil index, which also further identified the contribution elements of rural carbon emissions. The gradual growth of rural carbon emissions in China's provinces has been accompanied by a spatial clustering of high emissions, and the carbon emissions among the country's eight regions are characterized by large inter-regional and small intra-regional differences. By identifying the carbon emissions contributions of regions and the carbon sources, we found that the provinces in the central region produce the most emissions, with the top 3 of 11 provinces contributing up to 61.56% of the total national production. Furthermore, emissions from the dominant carbon source in rural China, raw coal, has decreased to 49.22%, and the low use of electricity and natural gas results in the structure of rural carbon sources being weakly decarbonized. The decomposition of carbon emissions indicated that rural economic development plays a prominent contributory role in carbon emissions, whereas energy consumption per unit output value has a significant inhibitory effect on carbon emissions. This study contributes to current carbon emission-related research by identifying the main contributors of rural carbon emissions from multiple perspectives.

Energy mix, financial development, and carbon emissions in China: a directed technical change perspective

Based on a two-sector (clean energy and dirty energy) model of directed technical change, we examine the relationship between carbon emissions, clean energy consumption, and financial development in China using the ARDL method. The results show that clean energy consumption reduces carbon emissions effectively but the effect of financial development is opposite, suggesting that financial development increases carbon emissions, contradicting the findings of many existing studies. Then, we decompose financial development on carbon emissions into two different effects: substitution and income effects. The substitution effect reflects more dirty energy consumption as a result of directed technological change promoted by financial development, leading to more carbon emissions. The income effect results in a decline in carbon emissions because financial development enables firms to use more clean energy. The empirical results indicate that the net effect of financial development has caused more carbon emissions and a 1% increase in financial development results in a 0.45-0.79% increase in carbon emissions. The policy implication is also discussed.

Effects of population flow on regional carbon emissions: evidence from China

Population flow can affect regional carbon emissions. Based on the analysis of the dual transmission mechanism of population flow and its effect on carbon emissions, this paper empirically studies the impact of population flow and other related factors on China's carbon emissions through panel econometric regression and heterogeneity analysis with fixed effect model. The results show that, firstly, in the long or short term, China's population flow can reduce the growth of carbon emissions. Secondly, the regional population aging and knowledge structure improvement caused by population flow are helpful to reduce carbon emissions, while the regional urbanization improvement caused by population flow is not significantly correlated with the growth of household miniaturization on carbon emissions. Thirdly, from the perspective of heterogeneous geographical divisions, population flow promotes the increase of carbon emissions in the northwest region of the Hu Huanyong Line (Hu Line), while it is opposite in the southeast region of Hu Line. Fourthly, China's consumption level, per capita GDP, energy intensity, and energy consumption structure have contributed to the growth of carbon emissions, while carbon intensity has a negative effect on carbon emissions. Finally, this paper puts forward relevant suggestions from the perspective of coordinating population policy and energy conservation and emission reduction policy.

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China's County-Level Cities

Improving carbon efficiency and reducing carbon intensity are effective means of mitigating climate change. Carbon emissions due to urban residential energy consumption have increased significantly; however, there is a lack of research on urban residential carbon intensity. This paper examines the spatiotemporal variation of carbon intensity in the residential sector during 2001-2015, and then identifies the causes of the variation by utilizing the logarithmic mean Divisia index (LMDI) with the help of Microsoft Excel 2016 for 620 county-level cities in 30 Chinese provinces. The results show that high carbon intensity is mainly found in large cities, such as Beijing, Tianjin, and Shanghai. However, these cities showed a downward trend in carbon intensity. In terms of influencing factors, the energy consumption per capita, urban sprawl, and land demand are the three most influential factors in determining the changes in carbon intensity. The effect of energy consumption per capita mainly increases the carbon intensity, and its impact is higher in the municipal districts of provincial capital cities than in other types of cities. Similarly, the urban sprawl effect also promotes increases in carbon intensity, and a higher degree of influence appears in large cities. However, as urban expansion plateaus, the effect of urban sprawl decreases. The land-demand effect reduces the carbon intensity, and the degree of influence of the land-demand effect on carbon intensity is also clearly stronger in big cities. Our findings show that lowering the energy consumption per capita and optimizing the land-use structure are a reasonable direction of efforts, and the effects of differences in influencing factors should be paid more attention to reduce carbon intensity.