A spatial–temporal decomposition approach to performance assessment in energy and emissions

Ang, B.W.; Su, Bin; Wang, H.

doi:10.1016/j.eneco.2016.08.024

Cited by 128 publications

(54 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Carbon sequestration market may not satisfy current China's emission demands due to uncertainties (Ang, Su & Wang, 2016;Chen et al, 2013;. found that government policies for increasing energy efficiency in fact are always lagged behand carbon emission reduction in China.…”

Section: Kg Of Standard Coal In 2014 the People's Republic Of China mentioning

confidence: 99%

The energy policy outlets for community acceptance of ecological investment in China

Wang

Deng

2017

Energy Policy

View full text Add to dashboard Cite

Policy implication about satisfactions of energy use and housing are much sensitive to social discount rate changes, which can increase the dissatisfaction of residential happiness because of an increasing price mechanism when 'per capita' resource faces to decline. We thus estimate the private expected rates of return on ecological investment to improve urban and peri-urban environmental infrastructures are about to 7.54% and 18.37% respectively. The endogeneities of income and saving rise can increase the uncertain part of private discount rate up to a higher estimated subjective social discount rate about 14.46% for urban or 8.86% for peri-urban environmental infrastructures improvement. The estimated time preference rate can be raised from 1‰ to 1.72‰. The prediction of these estimated private discount rate can ease at least 20% of the dissatisfaction to energy use and 10% of the dissatisfaction to housing conditions. Therefore, we suggest opening the landscape rights to individual willingness-to-invest, and providing options to let people pay a part of their pensions for temporal permits to living in some well-served villages where are close to the places with advanced environmental amenities and being supported by central planning policy via the crowdfunding operation for improving environmental quality.

show abstract

Section: Kg Of Standard Coal In 2014 the People's Republic Of China mentioning

confidence: 99%

The energy policy outlets for community acceptance of ecological investment in China

Wang

Deng

2017

Energy Policy

View full text Add to dashboard Cite

show abstract

“…Some research focus on the topics related to the decomposition of changes in the aggregate industrial energy intensity by using a decomposition technique (Ang & Wang, 2015;Chunbo, 2014;Roinioti & Koroneos, 2017;Wei, Ke, Zhou, & Gao, 2016). Furthermore, related methods are updated by investigating the possible linkages between the ratio measure (multiplicative decomposition) and the difference measure (additive decomposition) to better solve energy problems, including their decomposition results (Ang, Su, & Wang, 2016;Cjoi, & H.,, & Ang, B. W., 2012;Huang, Du, & Hao, 2017). There also exists some paper exploring the driving forces behind China's changing energy intensity by using decomposition and econometric analysis (Feng & Zheng, 2012).…”

Section: Introductionmentioning

confidence: 99%

Does urban haze pollution inversely drive down the energy intensity? A perspective from environmental regulation

Hou

Jing-hua

Chen

et al. 2019

Sustainable Development

View full text Add to dashboard Cite

Reducing energy intensity has consistently been advocated by the Chinese government in its effort to achieve sustainable development targets. Extant research suggests that energy consumption is often viewed as the primary cause of poor air quality. This paper investigates whether a certain threshold of environmental regulation causes the relationship to be reversed, that is, whether urban haze pollution could facilitate the reduction of energy intensity. On the basis of the city-level panel data within the period of 2010-2015 in China, we systematically analyze the determinants of energy intensity and empirically investigate the dynamic threshold effects of environmental regulation on the association between urban haze pollution and energy intensity. The results suggest that serious haze pollution does reduce energy intensity, but it is dependent on environmental regulation. In particular, weak environmental regulation fails to optimize energy usage. With environmental regulation passing a critical threshold, urban haze pollution significantly decreases energy intensity. Therefore, the heterogeneous effect of environmental regulation across different regions should be considered. We find evidence that air pollution could inversely drive down energy intensity under a certain threshold level of environmental regulation. This result provides insights to policy makers and may help them design and implement more efficient urban environmental policies. K E Y W O R D SChina, dynamic threshold model, energy intensity, environmental regulation, sustainable development, urban haze pollution

show abstract

“…to decrease the clinker share and increase in alternative fuels) and clinker trade, while the activity effect significantly contributes to the emission increase (Branger and Quirion, 2015). With growing interest in spatial and temporal analysis and considering the difference of performance assessment, Ang et al (2016) proposed three broad categories of decomposition analysis, namely single-country temporal analysis, multi-country temporal analysis, and cross-country analysis, while the spatial index decomposition analysis models are further divided into bilateral-region, radial-region, and multi-region models (Ang et al, 2016). The aim of spatial and temporal analysis is to better understand regional heterogeneity (e.g., the differences in population, economic development level, urbanization process, and energy efficiency) and for regional projections into the future.…”

Section: Spatial-temporal Decomposition Analysismentioning

confidence: 99%

“…The multi-regional spatial-temporal decomposition analysis can provide a comparison/benchmarking between each target region in different years and the group average (Ang et al, 2016(Ang et al, , 2015Su and Ang, 2016). As shown is Figure 1, the solid lines represent the direct comparisons between each of the Ri th province and the national average, while the dashed lines represent the indirect decomposition results.…”

Section: Multi-regional Spatial-temporal Decomposition Analysismentioning

confidence: 99%

Exploring the driving forces of energy consumption and environmental pollution in China's cement industry at the provincial level

Zhang

Wagner

2018

Journal of Cleaner Production

View full text Add to dashboard Cite

show abstract

A spatial–temporal decomposition approach to performance assessment in energy and emissions

Cited by 128 publications

References 29 publications

The energy policy outlets for community acceptance of ecological investment in China

The energy policy outlets for community acceptance of ecological investment in China

Does urban haze pollution inversely drive down the energy intensity? A perspective from environmental regulation

Exploring the driving forces of energy consumption and environmental pollution in China's cement industry at the provincial level

Contact Info

Product

Resources

About