China’s energy intensity target allocation needs improvement! Lessons from the convergence analysis of energy intensity across Chinese Provinces

Zhang, Pan; Xiao, Wang; Zhang, Nan; Wang, Yiyang

doi:10.1016/j.jclepro.2019.03.193

Cited by 30 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, there is a difference between absolute convergence and conditional convergence in the convergence analysis. No control variables need to be included when absolute convergence is examined, whereas control variables need to be included when conditional convergence is examined [ 56 , 57 ]. The purpose of this paper was to study the absolute convergence features of haze in various regions of China, so other economic and social development indicators were not included as control variables in the model.…”

Section: Discussionmentioning

confidence: 99%

“…We used convergence analysis, which emerged from economics, to examine the temporal variation of the mean annual PM 2.5 concentrations in various regions [ 56 , 57 ]. Using this approach, we could test if cities with higher starting PM 2.5 concentrations would have more sharp declines in yearly mean PM 2.5 concentrations than cities with lower initial ones.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Wang

Chen

Zhang

2022

IJERPH

Self Cite

View full text Add to dashboard Cite

Scientific study of the temporal and spatial distribution characteristics of haze is important for the governance of haze pollution and the formulation of environmental policies. This study used panel data of the concentrations of particulate matter sized < 2.5 μm (PM2.5) in 340 major cities from 1999 to 2016 to calculate the spatial distribution correlation by the spatial analysis method and test the temporal convergence of the urban PM2.5 concentration distribution using an econometric model. It found that the spatial autocorrelation of PM2.5 seemed positive, and this trend increased over time. The yearly concentrations of PM2.5 were converged, and the temporal convergence fluctuated under the influence of specific historical events and economic backgrounds. The spatial agglomeration effect of PM2.5 concentrations in adjacent areas weakened the temporal convergence of PM2.5 concentrations. This paper introduced policy implications for haze prevention and control.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Wang

Chen

Zhang

2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…As mentioned earlier, previous studies have found that PM 2.5 pollution can hinder economic growth [50] and threaten people’s physical health [11]. Thus, it is necessary to control PM 2.5 concentrations, especially setting mandatory performance targets [30,51], improving energy utilization efficiency [52,53], developing renewable energies [54,55] and updating the economic structure [9]. However, to control haze pollution needs high fiscal expenditure or even to slow down economic growth.…”

Section: Discussionmentioning

confidence: 99%

PM2.5 Concentrations and Subjective Well-Being: Longitudinal Evidence from Aggregated Panel Data from Chinese Provinces

Zhang

Wang

2019

IJERPH

Self Cite

View full text Add to dashboard Cite

Although haze pollution with PM2.5 as the chief pollutant has become a critical threat worldwide, little research has examined the effects of PM2.5 concentrations on subjective well-being. Based on a longitudinal aggregated panel dataset from Chinese provinces, this study investigates the effects of PM2.5 concentrations on levels of happiness and the inequality of happiness. The results showed that high ground-level PM2.5 concentrations decreased the average level of happiness and high PM2.5 concentrations had stronger negative effects on the happiness of persons with high income than those with low income. In addition, PM2.5 concentrations were also significantly negatively related to inequality of happiness in Chinese provinces. Further empirical tests showed that the negative effects of PM2.5 concentrations on the inequality of happiness could be explained by the stronger influence of PM2.5 concentrations on the subjective well-being of individuals with a higher initial level of happiness than those with a lower initial level of happiness. This confirms that PM2.5 pollution can do harm to subjective well-being and reduce variations in the subjective well-being of individuals. The policy implications of controlling haze pollution and improving well-being are discussed.

show abstract

“…Furthermore, given the various climate conditions and many different living habits, China's energy consumption generally presents different patterns among regions because of its vast area [20]. For this reason, this study initially performed a clustering analysis of China's provinces in terms of the growth pattern of household electricity consumption, and then recognized the driving factors of the consumption growth in each group.…”

Section: Driving Factor Selectionmentioning

confidence: 99%

What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis

Meng

Zhou

et al. 2019

Sustainability

View full text Add to dashboard Cite

The rapid growth of household electricity consumption is threatening the sustainable development of China’s economy and environment because of its impacts on the operation efficiency of the electric power system. To recognize the driving factors of the consumption growth and offer policy implications, based on the consumption-related data of 2015 and 2016, this research used the rank sum ratio (RSR) method to divide China’s 30 provinces into 5 groups and a logarithmic mean Divisia index (LMDI) algorithm to decompose the composition growth of each group into the quantitative contribution of each driving factor. The following conclusions were drawn from the empirical analysis. (1) The Yangtze basin is the most vigorous region of consumption growth and should be principally monitored. (2) Climate conditions have a remarkable impact on consumption growth and should be a key consideration when making differentiated household electricity policies. (3) The rebound effect has already appeared in a few of the most developed regions. Electricity price is an effective measure in dealing with this effect. (4) The improvement of the income level is the most important driving factor for consumption growth. (5) For provinces with high growth vitality, the change in the burden level of electricity expenditure prompts consumption growth. However, for provinces with low growth vitality, the situations are opposite. (6) The impacts of electricity price and population on consumption growth are negligible.

show abstract

China’s energy intensity target allocation needs improvement! Lessons from the convergence analysis of energy intensity across Chinese Provinces

Cited by 30 publications

References 39 publications

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

PM2.5 Concentrations and Subjective Well-Being: Longitudinal Evidence from Aggregated Panel Data from Chinese Provinces

What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis

Contact Info

Product

Resources

About