Decomposing the decoupling relationship between energy consumption and economic growth in China's agricultural sector

Analysis of energy consumption and change structure in major economic sectors of Pakistan

Studying and analyzing energy consumption and structural changes in Pakistan's major economic sectors is crucial for developing targeted strategies to improve energy efficiency, support sustainable economic growth, and enhance energy security. The logarithmic mean Divisia index (LMDI) method is applied to find the factors' effects that change sector-wise energy consumption from 1990 to 2019. The results show that: (1) the change in mixed energy and sectorial income shows a negative influence, while energy intensity (EI) and population have an increasing trend over the study period. (2) The EI effects of the industrial, agriculture and transport sectors are continuously rising, which is lowering the income potential of each sector. (3) The cumulative values for the industrial, agricultural, and transport sectors increased by 57.3, 5.3, and 79.7 during 2019. Finally, predicted outcomes show that until 2035, the industrial, agriculture, and transport incomes would change by -0.97%, 13%, and 65% if the energy situation remained the same. Moreover, this sector effect is the most crucial contributor to increasing or decreasing energy consumption, and the EI effect plays the dominant role in boosting economic output. Renewable energy technologies and indigenous energy sources can be used to conserve energy and sectorial productivity.

Impact of global value chain embedding on decoupling between China's CO2 emissions and economic growth: Based on Tapio decoupling and structural decomposition

With the increasing fragmentation of global production, China's participation in cross-border production sharing activities has had a considerable impact on the nation's economy and carbon dioxide (CO2) emissions. This study applied the Tapio model to quantitatively evaluate the decoupling between CO2 emissions and economic growth in China, dividing the decoupling index based on global value chains (GVCs) and domestic production within the IO framework, and introducing structural decomposition analysis (SDA) to analyze the GVC-related factors to the decoupling. The relevant research results are fourfold. (1) From 2000 to 2018, China achieved weak decoupling between emissions and economic growth. Domestic and GVC effects each had a negative impact on the decoupling; however, after 2008, the GVC effect had a promotional effect and the negative domestic effect declined. (2) Emission intensity was the primary factor promoting decoupling through domestic and GVC effects, while the scale of final demand was the main hindrance. And the negative effects of GVC-related factors declined following the economic crisis. (3) The regional and sectoral structures of GVC production (58.44 % and 56.08 %) had promotional roles in the changes in GVC effects, while GVC production linkages (-20.19 %) had hindering effects. Various factors contributed to the hindering effect from the 2008 to 2011 index, whereas from the 2011 to 2018 index, all factors contributed to the promotional effect. (4) From 2000 to 2018, the average annual global value chain effect promoted the low-carbon development of China's labor-intensive and knowledge-based manufacturing. In order for GVCs to play a positive role in decoupling, China should promote trade facilitation through international platforms, support the advancement of production technology, reasonably guide China's industries to participate in the regional and industrial links of GVCs, and develop strategic emerging industries.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Decoupling analysis and forecast of economic growth from electricity consumption in the Yangtze River Delta region, China

Decoupling economic growth from electricity consumption is essential for energy conservation and emission reduction. Firstly, this paper applies the LMDI decomposition model to analyze the driving factors of electricity consumption in the Yangtze River Delta region. Secondly, scenario analysis and Monte Carlo technique are combined to research the evolutionary trend of electricity consumption from 2020 to 2035, so as to further analyze the decoupling state. Finally, using nonparametric kernel density estimation, this paper studies the evolution trend of decoupling state from 2005 to 2035. The results show that (1) economic growth is the main factor that promotes the increase of total electricity consumption. Domestic intensity and population scale contribute to the increase in total electricity consumption. The primary factor inhibiting the increase of total electricity consumption is production intensity, while industrial structure and urbanization level contribute to the decrease in total electricity consumption. (2) From 2005 to 2035, the decoupling level has been optimizing on the whole, and the internal gap has also reduced, but there still exists obvious internal gap. (3) Under the three scenarios, the evolution trend of production and domestic electricity consumption is the same. During 2020-2035, the production and domestic electricity consumption both show an increasing trend, with the total electricity consumption under the baseline scenario being the highest, followed by the general and the enhanced electricity-saving scenario. Combined with the empirical results of this paper, some policy recommendations are proposed.