Asymmetric transfer effects among real output, energy consumption, and carbon emissions in China

China has a high frequency of natural disasters and it has become the economy with the largest carbon emissions in recent years. In this study, we mainly investigated the relationships between carbon emissions and natural disaster losses in China, as well as considering important factors such as economic growth and new energy consumption. Time series data for China from 2000 to 2020 were selected and based on the nonlinear auto-regressive distributed lag model method, a short-term error correction model and long-term co-integration relationship model were obtained between carbon emissions and their related factors. The results showed that in the long run, there is a significant nonlinear relationship between carbon emissions, new energy consumption and direct economic losses from natural disasters. There is a significant U-shaped relationship between natural disasters and carbon emissions, that is, natural disaster losses will significantly inhibit carbon emissions before they are below a certain threshold, but fewer natural disaster losses will increase carbon emissions. On the contrary, there is an inverted U-shaped relationship between new energy consumption and carbon emissions. When new energy consumption exceeds a certain threshold, it will help carbon peak early. In the short term, the impact of natural disasters on carbon emissions in the current period is significantly positive and higher than that in the lagged period, but the impact of its square term is negative. The short-term error correction model coefficient is −0.6467, and the error will be corrected when the short-term volatility deviates from the long-term equilibrium. These results suggest that attention should be paid to reducing disaster losses and the low-carbon reconstruction path for natural disasters, as well as continuously improving the level of new energy utilization, accelerating the pace of energy substitution, and promoting economic transformation for achieving “carbon peaking” in China.

After more than two decades of negotiation, the China–Russia gas deal represents a new era of energy cooperation between China and Russia. In total, this is a win–win deal for both sides. For China, the deal will decrease energy consumption and carbon emission but will not significantly influence air quality; for Russia, it will provide a new market for its gas resources. In this study, we calculated the energy consumption, carbon emission, and particulate matter pollution (PM2.5 and PM10) in China in 2020, 2030, 2040, and 2050 under four IPCC representative concentration pathways (RCPs 8.5, 6.0, 4.5, and 2.6). We found that energy consumption and carbon emission decreased under the gas deal in RCPs 8.5, 6.0, and 4.5, although the rate of decrease slowed over time; however, in RCP 2.6, the rate of decrease of energy consumption and emission increased over time. PM2.5 and PM10 emission showed similar trends but with increasing rate, although the gas deal would mitigate air pollution in the short term. Although China’s government hopes to reduce carbon and pollutant emission under the deal, our results suggest that additional mitigation measures will be necessary to achieve this goal. Nonetheless, the reduction in carbon emission suggests that the China–Russia gas deal provides a model that other countries can follow to slow climate change.

Economic structure, technology, consumption habits, cultural and similar values vary greatly between countries. Energy consumption included in this structure is theoretically thought to be related to growth output. Could slowing the growth be the cost of reducing energy consumption? In this study, using annual data between 1970 and 2019, it was investigated whether per capita energy consumption affects per capita income asymmetrically or not by using the NARDL model. The feature that makes this study different from similar studies is that it interprets the short and long-term asymmetric effects with the analysis model used and makes a unique contribution to the literature. The findings gave us the conclusion that income is affected in the same way by shocks experienced in energy consumption, and it has been observed that the effect of positive shocks is greater in the long run. However, we conclude that negative shocks are more effective than positive shocks in the short term. Thus, we see that the increase in energy consumption in the long term increases the per capita income, and the decrease in energy consumption has a high as well as negative impact on income per capita in the short term. Based on the fact that the conservation approach is managed in a balanced way, one of the reasons for the slowdown in the national income rates of the countries may be due to the decrease in energy consumption in the long term. Accepting that the conservation approach is managed in a balanced way, one of the reasons for the slowdown in the national income rates of the countries may be due to the decrease in energy consumption in the long term.Keywords: energy consumption, per capita income, economic growth, NARDL asymmetricJEL Classifications: Q43, P44, O47, C12DOI: https://doi.org/10.32479/ijeep.11428

The asymmetric influence of renewable energy and green innovation on carbon neutrality in China: Analysis from non-linear ARDL model

The emissions, energy consumption, and growth nexus: Evidence from the commonwealth of independent states

How do energy consumption and environmental regulation affect carbon emissions in China? New evidence from a dynamic threshold panel model

Based on the data from 1978-2010, this paper analyzes the causal relationships between carbon emissions, energy consumption, and economic growth in Shanghai, adopting the co-integration and vector error correction methods. The Grey prediction model is applied to forecast three variables for the period between 2011 and 2020. As the empirical results showed, in the long-run equilibrium, there is a positive relationship of a long-term equilibrium between carbon emission and energy consumption in Shanghai. However, between carbon emission and real GDP, there is a negative correlation. Besides, in the short-run equilibrium, energy consumption is the important impact on carbon emission. The causality results show that there is a bidirectional causality relationship between carbon emission, real GDP and energy consumption. For the purposes of reducing carbon emissions and not adversely affecting economic growth, Shanghai should optimize the structure of energy consumption and develop new energy. In addition, the optimal forecasting models of real GDP, energy consumption and carbon emissions have good prediction precision with MAPEs of less than 3%.

With accelerating urbanization, building sector has been becoming more important source of China’s total carbon emission. In this paper, we try to calculate the life-cycle carbon emission, analyze influencing factors of carbon emission, and assess the delinking index of carbon emission in China’s building sector. The results show: (i) Total carbon emission in China’s building industry increase from 984.69 million tons of CO2 in 2005 to 3753.98 million tons of CO2 in 2013. The average annual growth rate is 18.21% per year. Indirect carbon emission from building material consumption accounted to 96–99% of total carbon emission. (ii) The indirect emission intensity effect was leading contributor to change of carbon emission. The following was economic output effects, which always contributed to increase in carbon emission. Energy intensity effect and energy structure effect took negligible role to offset carbon emission. (iii) Delinking index show the status between carbon emission and economic output in China’s building industry during 2005–2006 and 2007–2008 was weak decoupling; during 2006–2007 and during 2008–2010 was expansive decoupling; and during 2010–2013 was expansive negative decoupling.

The impact of private sector energy investment, innovation and energy consumption on China's carbon emissions

Roadmapping green economic restructuring: A Ricardian gradient approach

The current paper seeks to examine the relationship between energy consumption and real output in the United Arab Emirates (UAE) economy. Technically, we assess the following two questions: ‘does the increase in energy consumption cause more economic growth in the UAE economy?’ or ‘does the rapid economic growth lead to more energy consumption?’ We utilise the bounds testing approach to cointegration and error correction model and Granger causality test to achieve the paper goal. The paper employs an annual data series of the UAE real gross domestic product and energy consumption over the 1973–2008 period. The findings of the paper have two levels: short‐run and long‐run. In the short‐run, the results reveal a clear statistically significant bidirectional positive relationship between energy consumption and real output in the UAE economy. However, in the long‐run the estimate illustrates only one relationship prevails from the short‐run. This one is the statistically significant positive unidirectional relationship running from real output to energy consumption.

The COVID-19 epidemic and the Russian–Ukrainian conflict have led to a global food and energy crisis, making the world aware of the importance of agroforestry development for a country. Modern agriculture mechanization leads to massive energy consumption and increased CO2 emissions. At the same time, China is facing serious demographic problems and a lack of consumption in the domestic market. The Chinese government is faced with the dilemma of balancing environmental protection with economic development in the context of the “double carbon” strategy. This article uses annual World Bank statistics from 1990 to 2020 to study the asymmetric relationships between agroforestry development, energy consumption, population size, and economic development on CO2 emissions in China using the partial least squares path model (PLS-PM), the autoregressive VAR vector time series model, and the Granger causality test. The results are as follows: (1) The relationship between economic development and carbon dioxide emissions, agroforestry development and carbon dioxide emissions, energy consumption and carbon dioxide emissions, and population size and carbon dioxide emissions are both direct and indirect, with an overall significant positive effect. There is a direct negative relationship between population size and carbon dioxide emissions. (2) The results of the Granger causality test show that economic development, energy consumption, and CO2 emissions are the causes of the development of agroforestry; economic development, agroforestry development, population size, and CO2 emissions are the causes of energy consumption; energy consumption is the cause of economic development and CO2 emissions; and agroforestry development is the cause of population size and energy consumption. (3) In the next three years, China’s agroforestry development will be influenced by the impulse response of economic development, energy consumption, and CO2 emission factors, showing a decreasing development trend. China’s energy consumption will be influenced by the impulse response of economic development, agroforestry development, population size, and CO2 emission factors, showing a decreasing development trend, followed by an increasing development trend. China’s CO2 emission will be influenced by the impulse response of energy consumption and agroforestry development. China’s CO2 emissions will be influenced by the impulse response of energy consumption and agroforestry development factors, showing a downward and then an upward development trend.

Transportation is an important source of carbon emissions in China. Reduction in carbon emissions in the transportation sector plays a key role in the success of China’s energy conservation and emissions reduction. This paper, for the first time, analyzes the drivers of carbon emissions in China’s transportation sector from 2000 to 2015 using the Generalized Divisia Index Method (GDIM). Based on this analysis, we use the improved Tapio model to estimate the decoupling elasticity between the development of China’s transportation industry and carbon emissions. The results show that: (1) the added value of transportation, energy consumption and per capita carbon emissions in transportation have always been major contributors to China’s carbon emissions from transportation. Energy carbon emission intensity is a key factor in reducing carbon emissions in transportation. The carbon intensity of the added value and the energy intensity have a continuous effect on carbon emissions in transportation; (2) compared with the increasing factors, the decreasing factors have a limited effect on inhibiting the increase in carbon emissions in China’s transportation industry; (3) compared with the total carbon emissions decoupling state, the per capita decoupling state can more accurately reflect the relationship between transportation and carbon emissions in China. The state of decoupling between the development of the transportation industry and carbon emissions in China is relatively poor, with a worsening trend after a short period of improvement; (4) the decoupling of transportation and carbon emissions has made energy-saving elasticity more important than the per capita emissions reduction elasticity effect. Based on the conclusions of this study, this paper puts forward some policy suggestions for reducing carbon emissions in the transportation industry.

The development of the real estate industry inevitably consumes large amounts of fossil energy and makes great contributions to China’s carbon emissions. However, very few research studies have explored the intrinsic link and influence mechanisms between the rapidly growing real estate sector and carbon emissions in China. Hence, this study investigated the impact of real estate development on carbon emissions using a differential generalized method of moments and dynamic panel threshold models. The empirical results show that: (1) There is a non-linear relationship between real estate development and China’s carbon emissions, first promoting and then inhibiting them with the increasing level of real estate development, but it will take a long time to reach the latter stage in the future; (2) The threshold effect of economic development levels on carbon emissions was identified with a threshold value of 9.904, and the positive impact of real estate development on China’s carbon emissions is more significant in economically backward areas; (3) The threshold effect of population sizes on carbon emissions was identified with a threshold value of 7.839, and in areas with larger populations, the positive impact of real estate development on China’s carbon emissions is more significant. The findings above extend the carbon emission literature by clarifying the threshold role of the economic development level and population size between real estate development and carbon emissions. Furthermore, the findings of this study are instructive for China to formulate energy-saving and emission-reduction policies according to local conditions and will ultimately contribute to achieving the goal of “carbon peaking” and “carbon neutrality”.

Since the early 2010s, building energy consumption in regions in China with hot summers and cold winters has experienced an average annual growth rate of 6.5%, while building carbon emissions demonstrated an average annual growth rate of 3.7%. This underscores the pressing need to reduce building energy consumption and carbon emissions. The layout of residential clusters plays a critical role in determining the effective shading coefficient, which directly impacts solar radiation gain and subsequently affects energy consumption and carbon emissions. To explore this correlation and optimize the layout configuration of residential clusters to achieve the objective of minimizing energy consumption and carbon emissions in buildings, our study employed ECOTECT 2011 software to assess the layout attributes of different residential clusters through an analysis of the effective shading coefficient. Furthermore, using VirVil-HTB2 17_04_21 software, we simulated the solar radiation, building energy consumption, and carbon emissions for different residential cluster layouts. To examine the interplay between solar radiation, energy consumption, and carbon emissions, SPSS 27 software was used. The findings revealed that different residential cluster configurations exhibit unique effective shading coefficients, substantially influencing the solar radiation received by buildings and, consequently, their thermal performance. Our research reveals that adopting a staggered layout can lead to a reduction in average operating energy consumption by up to 2.23% and cooling energy consumption by up to 7.17%, compared to an enclosed layout. Similarly, enclosed layouts can contribute to a decrease in heating energy consumption by up to 4.06%, in contrast to courtyard layouts. Additionally, scattered layouts can effectively reduce carbon emissions by up to 0.95% when compared to courtyard layouts.