Residential Carbon Emissions Research Articles

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks.

Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.

Nonlinear influence of urbanization on China's urban residential building carbon emissions: New evidence from panel threshold model

Carbon mitigation in the urban residential building sector is critical for China to achieve its carbon peak and carbon neutral commitment. However, how urbanization affects urban residential building carbon emissions is still unclear. This study adopts the panel threshold regression model to explore the dynamic influence mechanism of the urbanization on urban residential building CO2 emissions based on the evidence from China's 30 provincial regions during 2000–2015. Results indicate that urbanization contributes positively to the increase of urban residential building CO2 emissions, while such degree of influence varies across different stages of income and energy structure. As for per capita income, the promoting effect of the urbanization on urban residential building CO2 emissions is enhanced with the growth of per capita income. And the degree of such increasing effect becomes greater when per capita income exceeds its threshold value. Regarding the energy mix, the driving effect of urbanization on urban residential building CO2 emissions is also strengthened when the energy mix crosses its threshold value, showing a “stepwise growth” feature. This study reveals the nonlinear influence mechanism of urbanization on urban building CO2 emissions, and this is helpful in boosting the related theoretical and practical exploration on the impact of urbanization on the environment. Based on our findings, an environmentally-friendly consumption pattern should be promoted and more penetration of cleaner energies should be improved in urban households, which will be effective to alleviate the increase of residential carbon emissions.

Dosis facit effectum why the size of the carbon tax matters: Evidence from the Swedish residential sector

Abstract After 25 years of gradually increasing its carbon tax, Sweden now imposes the world's highest tax on carbon dioxide emissions. This paper examines the effect of the Swedish carbon tax on residential carbon emissions by using European panel data. We employ synthetic control methods (SCMs) and perform difference-in-difference (DiD) regressions to evaluate the causal impact of carbon taxation on carbon emissions in the residential sector. We provide evidence that early-2000s carbon tax augmentation had a causal effect on residential carbon emissions. We find that the reduction of residential carbon emissions stemming from the carbon tax augmentation ranges from 200 kg (when compared with other countries with a carbon tax of more than 20 euro) to 800 kg (when compared with countries without a carbon tax) of CO2 per capita per year. Hence, the evidence suggests that carbon taxation is an effective tool for reducing residential CO2 emissions and, in turn, mitigating climate change.

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

Effective strategies, policies and measures for carbon emission reduction need to be developed and implemented according to good understanding of both local conditions and spatial differentiation mechanism of energy consumption associated with human activities at high resolution. In the study, we first collected statistical yearbooks, high resolution remotely sensed imageries, and 3895 usable questionnaires for the urban areas of Kaifeng; then measured the carbon emissions from household energy consumption, using the accounting method provided in the IPCC GHG Inventory Guidelines; and finally applied both exploratory and explanatory statistical methods to characterize the spatial pattern of carbon emissions at high resolution, identify key influencing factors, and gain better understanding of the spatial differentiation mechanism of urban residential carbon emissions. Our study reached the following conclusions: (1) Central heating facilities with controllable flow are important for carbon emissions reduction, but its spatial distribution shows unfairness; (2) Spatial clusters of high carbon emission areas were primarily located in the outer suburbs of the city, validated to some extent the hypothesis that urban sprawl has a driving effect on the increasing urban residential carbon emissions; (3) Factors like size of residential area, family structure, life style, personal preference and behavior rather than household income have significant impacts on household carbon emissions, implying that effective control of residential areas, promotion of family life and low-carbon lifestyle, and effective guidance of proper behaviors and preferences will play a crucial role in reducing urban residential carbon emissions; and (4) Most of the identified influencing factors exhibit clear and specific spatial patterns and gradients of impact, implying that measures for urban residential carbon emission reduction should be adapted to location conditions. The study has generated a set of concrete evidences and improved understandings of the spatially differentiated mechanisms upon which the formation and deployment of any effective strategies, policies and measures for reducing urban residential carbon emissions should be based.

Decomposition and prediction of direct residential carbon emission indicators in Guangdong Province of China

Abstract A direct residential carbon emission (DRCE) indicators calculation model and influence factor decomposition model were established in the Guangdong Province of China to study the dynamic changes and influence factors of DRCE from 1995 to 2017 respectively. A system dynamics prediction model was also established to predict future development trends of DRCE. The results showed that the DRCE in Guangdong Province had grown linearly over the years and presented an obvious urban–rural dual structure. The growth rates of urban and rural DRCE were relatively close, resulting in little change in the ratio between the two. However, in 2010, the per capita rural DRCE began to exceed the per capita urban DRCE. By 2017, the per capita rural DRCE was 1.35 times that of urban DRCE, which is obviously different from other provinces in China. The analysis of the influence factors of DRCE in Guangdong Province shows that the per capita consumption expenditure has a significant positive effect on DRCE, followed by the size of the population. Energy price is the main negative influence factor, followed by the energy carbon emission factor. The energy consumption structure and urban and rural population structure have a little effect on DRCE. Based on the system dynamics method, the results obtained by the DRCE prediction model show that the per capita urban and rural DRCE in Guangdong Province will be 1.06 tCO2e and 2.22 tCO2e, respectively by 2030. With the continuous growth of population in Guangdong Province, the DRCE in Guangdong will increase, but the increase in the aging population will have a certain inhibitory effect on long-term carbon emissions.

Carbon emission quantification and decarbonization policy exploration for the household sector - Evidence from 51 Japanese cities

The household sector has been identified as a major driver of the economy and an important contributor to greenhouse gas (GHG) emissions. It is also pointed out by existing studies that influential factors of household emissions range from socioeconomic to geographic aspects. Promoting efficient and feasible decarbonization actions therefore calls for the quantification of the variability in emission patterns and its relationship with contextual factors. In this study, using data on monthly household expenditures at the city level, direct and indirect carbon emissions resulting from household consumption in 51 major Japanese cities in 2011 are calculated. A regression analysis is also conducted to explore the relationship between emissions from the household sector and the socioeconomic, demographic, and climatic characteristics of Japanese cities. Our results indicate seasonality and categorical differences in residential carbon emissions for cities in Japan. We also find per capita income, urban population density, and proportion of the population over 65 years old to be the most influential variables that shape the residential emission landscape of Japanese cities. We thus suggest that seasonality, categories of consumption, and city characteristics be considered as crucial factors in policy making for emission mitigation targeting consumption behavior of urban residents.

Impact of urbanization, economic growth, and population size on residential carbon emissions in the SAARC countries

This study attempts to analyse the influence of urbanization, economic growth, and population size on residential carbon emissions in the South Asian Association for Regional Cooperation (SAARC) member nations for the period 1994 to 2013. To do so, we employ an augmented STIRPAT model in an ecological analytic framework with a fixed effect regression model that incorporates Driscoll and Kraay standard errors. The results show that population size and per capita GDP are leading drivers of high carbon emissions in the SAARC countries. The empirical results show that a U-shaped relationship exists between urbanization and residential carbon emissions: residential carbon emissions initially decrease with an increase in urbanization, reach a turning point at 25.33% and then increase with urbanization. Thus, our findings suggest a need for stronger national policies to control the impact of urbanization and population growth on residential carbon emissions in the SAARC member countries.

Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions

This paper analyzes the relationship between urban form, shrinking cities, and residential carbon emissions, based on information collected for prefectural-level and above Chinese cities for the years of 2005, 2010, and 2015. After controlling for a number of urban form and socioeconomic variables (e.g., size, compactness, and polycentricity), this paper pays attention to residential carbon emissions in ‘shrinking cities’, which have experienced population loss and are a recent urban phenomenon in China. Everything else being equal, shrinking cities tend to be associated with less energy efficient than their growing counterparts, suggesting that these cities may not only be ‘battling’ with shrinking populations and economies but also need to consider the environmental issues.

Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions

In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.

Residential carbon emission embedded in China's inter-provincial population migration

Population migration embodies virtual residential energy consumption and carbon transfer from the origin to the destination. Based on the differences of the per capita levels between the sending-out origins and arriving-in destinations, we develop a model to estimate the inter-spatial transfer flows of residential carbon emissions, broken down by rural-to-urban, rural-to-rural, urban-to-rural and urban-to-urban flows. The net value of transfer-in and transfer-out residential carbon emission contributes to the change of the whole carbon emission. Using the latest census data and energy balance sheet in 2010, China's inter-provincial residential carbon emission flows embedded in the population migration were calculated and visualized. We found that China's non-Hukou migration increased the national total residential carbon emission. The largest transfer flows were mainly from central to eastern China. The northern provinces were also distinct destinations due to the high-carbon energy structure. The regional difference of residential energy consumption structures, the unbalanced regional economic development and origin-to-destination interaction were the main influencing factors. To promote low-carbon and environment-friendly urbanization, the energy optimization policy should be enhanced in the identified regions, especially in the Beijing-Tianjin area.

Why the Scope of the Carbon Tax Matters – Evidence from the Swedish Residential Sector

Sweden has gradually increased its carbon tax within the past 25 years and imposes the world’s highest tax on carbon dioxide emissions today. This paper examines the impact of the Swedish carbon tax on residential carbon emissions. We perform Difference-in-Differences (DiD) regressions and Synthetic Control Methods (SCM) in order to evaluate the causal impact of carbon taxation on carbon emissions in the residential sector. Both methods provide evidence for a causal effect of the carbon tax augmentation in the early 2000s on residential carbon emissions. We find that the scope of the reduction of residential carbon emissions due to the carbon tax augmentation ranges between 200 kg (when compared to other countries with a carbon tax of more than 20 Euros implemented) and 800 kg of CO2 per capita per year (when compared to countries without a carbon tax). Hence, the evidence points towards the effectiveness of carbon taxation in reducing residential CO2 emissions and, thus, mitigating climate change.

Features and drivers for energy-related carbon emissions in mega city: The case of Guangzhou, China based on an extended LMDI model.

Based on the apparent energy consumption data, a systematic and comprehensive city-level total carbon accounting approach was established and applied in Guangzhou, China. A newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector. Research results are listed as follow: (1) Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou. (2) The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods, namely, the 10th five-year plan period (2003–2005), the 11th five-year plan period (2005–2010), and the 12th five-year plan period (2010–2013). The main reasons underlying these influencing mechanisms were different policy measures announced by the central and local government during the different five-year plan periods. (3) The affluence effect (g-effect) was the dominant positive effect in driving emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing emissions growth at the industrial sector. (4) The affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing emissions growth at the residential sector.

Temporal and spatial differences in carbon emissions in the Pearl River Delta based on multi-resolution emission inventory modeling

Carbon emissions research in rapidly urbanizing areas around the world has typically focused on numerical changes in regional carbon emissions, and less research has been conducted on the spatial and temporal characteristics of carbon emissions from different sources in the urban system. Here, based on Multi-Resolution Emission Inventory for China (MEIC) modeling data, carbon emissions from industrial, power generation, residential, and transportation sources were analyzed in the Pearl River Delta region from 2008 to 2012, and the corresponding spatiotemporal distributions and main influencing mechanisms were elucidated. The results were as follows. (1) From 2008 to 2012, the total amount of carbon emissions increased gradually, and carbon emissions per unit area increased from 6513.65 × 103 gC∙m−2∙a−1 to 7519.03 × 103 gC∙m−2∙a−1, where industrial carbon emissions were the highest, followed by power and transportation emissions; residential carbon emissions were the lowest. (2) The spatial characteristics of carbon emissions in the Pearl River Delta region showed a carbon emissions belt with a shape similar to that of the number seven and Shenzhen carbon emissions center. Industrial carbon emissions resembled a “one belt, one center” pattern. Power carbon emissions mainly showed a “seven shape” pertaining to a carbon emissions belt and secondary carbon emissions centers. Residential carbon emissions were mainly located in the Guangzhou–Foshan carbon emissions belt and Shenzhen secondary center. Lastly, transportation-related carbon emissions were mainly located in the Guangzhou–Foshan and Shenzhen–Dongguan carbon emissions belt. (3) From 2008 to 2012, carbon emissions from industrial, transportation, and residential sources all showed obvious spatial distribution characteristics related to the carbon belt of Guangzhou–Foshan, and the two carbon emissions centers displayed a centripetal agglomeration development trend, in which the emissions gradually expanded out from around this area. The reasons were attributed to the influences of the “Guangfo One City” policy, whereby joint development in Guangzhou and Foshan is being focused on in its entirety. (4) The future development direction of the Pearl River Delta region should be based on controlling the scale of urban development, construction of low-carbon industrial production areas, and low-carbon-oriented land use planning; the formulation of such reasonable measures would help to reduce future carbon emissions.

Spatial Patterns and Driving Factors of Urban Residential Embedded Carbon Emissions: An Empirical Study in Kaifeng, China

With the continuous improvement in living standards and great changes in lifestyles, more attention is being paid to the embedded carbon emissions produced by human consumption. With large sample data and high-resolution remote sensing images, we explored the spatial differentiation and influencing factors of household embedded carbon emissions within the city fine scale using the EIO-LCA model, spatial autocorrelation analysis and standard deviation ellipse, quantile regression, etc. The results indicate that the spatial dependence is more obvious than the characteristics of spatial heterogeneity; the high-value area of household embedded carbon emissions gathers in new development zones in cities that are expanding rapidly, mainly with residents in large number of newly-built commercial housing families and the relative’s courtyard of institutions. The factors of family characteristics, housing characteristics, lifestyles, and consumption concept have significant effects on the embedded carbon emissions of each person. The influencing intensity of most factors showed an increasing trend with increased carbon emissions. The study verified the impact of urban sprawl on residential carbon emissions and the applicability of the situated lifestyles theory in the construction of urban low-carbon communities in China.

The influencing factors of urban household embedded carbon emissions based on quantile regression

The embedded carbon emissions produced by human consumption are an important part of residential carbon emissions. With large sample data and high-resolution remote sensing images, we explored the spatial differentiation and influencing factors of household embedded carbon emissions within the city fine scale using the EIO-LCA model and quantile regression. The results indicate that the factors of family characteristics, housing characteristics, lifestyles, and consumption concept have significant effects on the embedded carbon emissions of each person. The influencing intensity of most factors showed an increasing trend with increased carbon emissions. The application of the quantile regression (QR) method can more accurately reveal the different mechanisms of various influencing factors under different distribution functions and overcome the estimation error of the least square method on the mean level. The estimated results show that the influence strength of the remaining factors on embedded carbon emissions per capita showed a trend of increase with the increase in carbon emissions, but the influencing strength of housing types and frugal purpose for saving money have the tendency of zigzag fluctuation. To a certain extent, the result emphasized the necessity of specific emissions reduction measures for high carbon emissions groups.

Revealing Monthly Urban Carbon Leakage Generated from Residential Energy Consumption

As cities have an increasingly important role in carbon reduction programs, a good understanding of urban energy usage and associated emissions are hence expected to contribute significantly toward policy aimed at reducing energy use and emissions. In this study, residential carbon footprint is revealed by calculating direct and indirect energy demands. The results show the seasonal variation of each type of energy, as well as carbon emissions leaked across city boundaries. The output indicates that 0.35 tC of emissions were generated per household in Tokyo in 2011, including 0.20 tC generated locally and 0.15 tC generated outside of Tokyo. For carbon leakage, February and October are found to result in the highest total emissions, with leaked emissions found to exceed local emissions from July to October. Based on these results, the obvious solution to reduce residential carbon emissions would be a shift to renewable energy sources away from gas use within Tokyo and power generation outside of Tokyo.

Spatial differentiation of daily travel carbon emissions in small- and medium-sized cities: An empirical study in Kaifeng, China

Abstract Issues relating to low-carbon transportation have received increasing attention in recent years for two competing reasons: the effort to slow down global warming and the upward trend in private vehicle ownership in China. Residential carbon emissions and the mechanisms influencing them have been adequately addressed by the published literature. However, both the built environment and socio-economic factors are spatially heterogeneous within cities, and this can be expected to lead to a corresponding spatial heterogeneity in residential carbon emissions. To better characterize spatial heterogeneity in the mechanisms influencing carbon emissions, this study collected fine-scale spatially-distributed data pertaining to carbon emissions arising from the travel activities of residents in Kaifeng City. The spatial characteristics of and factors influencing household travel carbon emissions were then investigated using spatial auto-correlation analysis and the geographical weighted regression (GWR) method. The results show that areas with high values of household travel carbon emissions were primarily in the new urban development zone and in the more rapidly-expanding built-up areas in the outer layer of the city. Additionally, a large average distance between working and living locations in the newly-built commercial housing communities in the rapidly urbanizing areas is a leading factor increasing resident travel carbon emissions. Meanwhile, the long average distances traveled for schooling on account of the currently imbalanced development of education resources is another leading factor for increasing residential travel carbon emissions. The conclusions of this research elucidate the spatial heterogeneity in the mechanisms influencing residential travel carbon emissions within the city. In particular, this study confirms that urban sprawl is a leading factor in driving residential travel behavior and thus carbon emissions. More importantly, this study demonstrates that the documented inequality in resident travel-related carbon emissions does not result from economic (e.g., household income) inequality. Instead, it is caused by the existence of inequality in the allocation of socioeconomic infrastructure (e.g., schools). It is our expectation that the results of this study can be used as a valuable reference for planning low-carbon cities.

Spatial Pattern of Residential Carbon Dioxide Emissions in a Rapidly Urbanizing Chinese City and Its Mismatch Effect

Cities undergoing rapid urbanization are characterized by quick successions of spatiotemporal patterns, meaning that traditional methods cannot adequately assess carbon emissions from urban residential areas, which prevents the study of spatial mismatch. Therefore, this study utilizes night-time lights to construct a spatial emissions model that enables the analysis of the evolution of emissions patterns in China. The results indicate that, compared to the traditional method, the spatial modeling based on night-time lights reflects the spatial emissions trajectories in a more timely and accurate manner in rapidly urbanizing cities. Additionally, we found a relatively low degree of spatial match between emissions and economic activities, with the former, which are greatly affected by urbanization, having a larger dynamism and instability than the latter. Such spatial mismatch effect illustrates that policy makers should focus on factors beyond economics in order to reduce residential carbon emissions during China’s rapid urbanization process.

Residential carbon dioxide emissions at the urban scale for county-level cities in China: A comparative study of nighttime light data

Abstract Carbon emissions from residential energy consumption in urban areas play a pivotal role in meeting emission targets. It is more beneficial to decomposing the emission reduction target to subnational units. This study aims to map urban residential carbon emissions at a finer spatial resolution to offer reference to disaggregating the carbon-reduction targets down to each sub-unit. With the launch of Suomi National Polar-orbiting Partnership (NPP) satellite, the day-night band of Visible Infrared Imaging Radiometer Suite (VIIRS) onboard represents a major advancement compared with Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) in terms of radiometric accuracy, spatial resolution and geometric quality. Therefore, the useful proxy was utilized to model the spatial distribution of residential carbon emissions at the urban scale for county-level cities in China. Firstly, the dynamic optimal threshold method was used to extract urban built-up areas based on NPP-VIIRS data and the statistical data. Secondly, comparison was conducted between the NPP-VIIRS data and both original and saturation-corrected DMSP-OLS data to explore the performance of NPP-VIIRS data. Finally, the spatial characteristics were analyzed in detail at the regional, provincial and prefectural level, respectively. The results show that the NPP-VIIRS data performed better in both statistic regression and spatial comparison. The spatial patterns indicated that there was an obvious north-south differentiation, especially the carbon emission density (defined as carbon emissions per grid), which was significantly higher in Northeastern China than that of other regions. The climate elements positively contributed to the increase of carbon emissions in residential sector, especially stimulating emissions related to building heating.

Estimation of Carbon Emissions of Domestic Energy Usage by Chinese Residents Based on Input - Output Method

This paper will be based on the existing literature and research methods, with the 1980 to 2014 China’s residential carbon emissions estimates. This paper mainly examines the changes in total residential carbon emissions and per capita carbon emissions, energy consumption per unit of household energy consumption, carbon emissions and energy composition of the above-mentioned analysis of urban and rural compared with the international comparison.