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Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

In China, environmental pollution responsibilities are divided according to administrative regions. However, because of the strong externality of environmental pollution, the movement of environmental pollution undoubtedly increases the complexity of pollution governance. To divide the responsibility of environmental pollution governance in each province, we effectively quantify the unequal relationship between environmental pollution costs and economic benefits in each province to understand the mechanism and characteristics of inter-provincial environmental pollution movement. Based on the regional input–output model and an inventory we compiled of sulfur dioxide(SO2) emissions of air pollutants in 2012, we calculate the implied inter-provincial environmental pollution emissions and economic benefits from trade, based on production and consumption (supply-side and demand-side). In addition, the movement relationship is explored, and the cost and economic value-added indexes of air pollution control are further constructed to provide effective evidence for a reasonable division of responsibility for environmental pollution control. The results show that there are obvious environmental inequities in the regional trade process in each province, indicating that environmental pollution has been moved. Developed provinces have more economic benefits but pay less in the process of trading goods with less-developed provinces due to the advantages of their industrial structure, while the opposite is true for less-developed provinces. Finally, we propose corresponding policy recommendations to change this condition.

In China, environmental pollution responsibilities are divided according to administrative regions. However, because of the strong externality of environmental pollution, the movement of environmental pollution undoubtedly increases the complexity of pollution governance. To divide the responsibility of environmental pollution governance in each province, we effectively quantify the unequal relationship between environmental pollution costs and economic benefits in each province to understand the mechanism and characteristics of inter-provincial environmental pollution movement. Based on the regional input–output model and an inventory we compiled of sulfur dioxide(SO2) emissions of air pollutants in 2012, we calculate the implied inter-provincial environmental pollution emissions and economic benefits from trade, based on production and consumption (supply-side and demand-side). In addition, the movement relationship is explored, and the cost and economic value-added indexes of air pollution control are further constructed to provide effective evidence for a reasonable division of responsibility for environmental pollution control. The results show that there are obvious environmental inequities in the regional trade process in each province, indicating that environmental pollution has been moved. Developed provinces have more economic benefits but pay less in the process of trading goods with less-developed provinces due to the advantages of their industrial structure, while the opposite is true for less-developed provinces. Finally, we propose corresponding policy recommendations to change this condition.

The electricity consumption and economic growth are highly correlated. The financial crisis in 2008 brought a negative effect on China’s economic growth, which also influenced the electricity consumption. The electricity demand of North China region was also greatly influenced by this financial crisis, the whole social electricity consumption growth rate of which decreased by 14.31% in 2008 compared to that in 2007. In order to analyze the random impulse effect of the financial crisis on the demand of electricity in North China, the monthly data is decomposed into deterministic trend, stochastic impact effect, and periodic trend using the Beveridge-Nelson decomposition method. After comparatively analyzing the impulse effect of the financial crisis on electricity consumption of six provinces in North China, we can draw the conclusions: (1) the electricity consumption of the whole society and the secondary industry are under larger negative impacts, and the random impulse effect of the secondary industry is more intense; and (2) the impact of the financial crisis on the tertiary industry, which is mainly influenced by seasonal changes, is smaller. Finally, some policy implications are proposed.

The electricity consumption and economic growth are highly correlated. The financial crisis in 2008 brought a negative effect on China’s economic growth, which also influenced the electricity consumption. The electricity demand of North China region was also greatly influenced by this financial crisis, the whole social electricity consumption growth rate of which decreased by 14.31% in 2008 compared to that in 2007. In order to analyze the random impulse effect of the financial crisis on the demand of electricity in North China, the monthly data is decomposed into deterministic trend, stochastic impact effect, and periodic trend using the Beveridge-Nelson decomposition method. After comparatively analyzing the impulse effect of the financial crisis on electricity consumption of six provinces in North China, we can draw the conclusions: (1) the electricity consumption of the whole society and the secondary industry are under larger negative impacts, and the random impulse effect of the secondary industry is more intense; and (2) the impact of the financial crisis on the tertiary industry, which is mainly influenced by seasonal changes, is smaller. Finally, some policy implications are proposed.

The primary objective of this research was to determine the impact of renewable energy, alternative and nuclear energy, urbanization, energy use, and fossil fuel energy consumption on Romanian economic development. To investigate the relation between variables, we employed the Autoregressive Distributed Lag (ARDL) technique in conjunction with FMOLS (Fully Modified Least Squares) and CCR (Canonical Cointegrating Regression). Long-run and short-run findings suggest that alternative and nuclear energy, as well as fossil fuel consumption, has a positive association with economic growth, but renewable energy, urbanization, and energy usage have an adversative relationship with economic growth. Similarly, FMOLS and CCR statistics indicate that alternative and nuclear energy and fossil fuel consumption have a favorable impact on economic development. Renewable energy consumption, urbanization, and energy use, on the other hand, revealed a negative connection with economic progress. Conservative solutions are necessary to implement appropriate policies to address energy consumption concerns in Romania in order to improve economic development.

The primary objective of this research was to determine the impact of renewable energy, alternative and nuclear energy, urbanization, energy use, and fossil fuel energy consumption on Romanian economic development. To investigate the relation between variables, we employed the Autoregressive Distributed Lag (ARDL) technique in conjunction with FMOLS (Fully Modified Least Squares) and CCR (Canonical Cointegrating Regression). Long-run and short-run findings suggest that alternative and nuclear energy, as well as fossil fuel consumption, has a positive association with economic growth, but renewable energy, urbanization, and energy usage have an adversative relationship with economic growth. Similarly, FMOLS and CCR statistics indicate that alternative and nuclear energy and fossil fuel consumption have a favorable impact on economic development. Renewable energy consumption, urbanization, and energy use, on the other hand, revealed a negative connection with economic progress. Conservative solutions are necessary to implement appropriate policies to address energy consumption concerns in Romania in order to improve economic development.