How will climate change affect the peak electricity load? Evidence from China

Chen, Hao; Yan, Haobo; Gong, Kai; Yuan, Xigang

doi:10.1016/j.jclepro.2021.129080

Cited by 18 publications

(4 citation statements)

References 37 publications

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“…However, the data used in this paper are daily data, the study is micro. Previous studies have proved that time dummy variables are not applicable to micro data processing, should be replaced by time trend variables ( 43 – 45 ).…”

Section: Theorymentioning

confidence: 99%

The Impact of China's Paired Assistance Policy on the COVID-19 Crisis—An Empirical Case Study of Hubei Province

Huang

Yao²,

Chen

et al. 2022

Front. Public Health

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To control the coronavirus pandemic (COVID-19), China implemented the Paired Assistance Policy (PAP). Local responders in 16 cities in Hubei Province were paired with expert teams from 19 provinces and municipalities. Fully supported by the country's top-down political system, PAP played a significant role in alleviating the COVID-19 pandemic in Hubei Province and China as a whole. In this study, we examined PAP using a two-way fixed effects model with the cumulative number of medical support personnel and cumulative duration as measurements. The results show personnel and material support played an active role in the nation's response to the COVID-19 public health crisis.

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Section: Theorymentioning

confidence: 99%

The Impact of China's Paired Assistance Policy on the COVID-19 Crisis—An Empirical Case Study of Hubei Province

Huang

Yao²,

Chen

et al. 2022

Front. Public Health

Self Cite

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“…The researchers primarily examined it through various types of social influences (normative and informational), moral norms and informational influences (i.e., trust in friends/relatives and neighbors), and attitudes towards target behavior on predictors of intention towards energy efficiency. The theories that supervise and are usually used are the theory of "Theory Plan Behavior (TPB)" (Ajzen, 1991;Lynch and Martin, 2013;Chen et al, 2021;Prete et al, 2017;and Wang et al, 2017). These researchers found that the TPB is a plausible model for explaining energy use intentions and behaviors.…”

Section: Literature Researchmentioning

confidence: 99%

Measurement of the Rebound Effect on Urban Household Energy Consumption Savings

Sudarmaji

Ambarwati

Munira

2022

IJEEP

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This research investigated consumer behavior patterns on energy conservation and energy efficiency. We investigated the factors that influence the rebound effect based on common characteristics and Socio-economic household characteristics in urban communities. This paper also explored the conservation of electricity through the idea of "nudge." The authors used the word "encouragement" in the chosen architectural program, designed to reduce household electricity consumption so that the rebound effect did not occur in household consumption. By utilizing the 'social norms and curtailment,' households given a "nudge" can save more on their electricity consumption costs. The condition of "nudge" that we included was through information on the costs incurred by households, 'social norms and curtailment,' and energy efficiency through replacing conventional lamps with energy-efficient lamps. The research took place in urban areas in the Bantul, Jogjakarta, between December 2021 and April 2022. Sixty-two respondents divide into two groups of households, i.e., observe households (self-selected) and control households (randomly selected). Both observe and control households must have an active electricity account for at least one year and have owned a house between 50 and 200 square meters. The model used a t-paired sample through the "Non-Equivalent Groups Design" (NEGD) framework for the two comparisons. The research found that 'social norms and curtailment' can reduce household electricity consumption, has cost savings over electricity consumption on average to 16.3049% for three months, and no rebound effect on savings happened.

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“…Caused by global warming, peak load will have the potential to keep rising. Reference [ 28 ] analyzes the relationship between China's yearly peak load and highest temperature, drawing a conclusion that each 1 °C increase in maximum temperature increases peak load by an average of 0.385 GW. Reference [ 29 ] analyzes the climate data from 30 provinces between the year 1995 and 2016, forecasting that load will grow by 0.094% or 0.061% for every 1% increase in CDD or HDD.…”

Section: Introductionmentioning

confidence: 99%

Towards the prospect of carbon-neutral power system 2060: A Power-Meteorology-Society systematic view

Ming,

Lin,

Gao

et al. 2024

Heliyon

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How will climate change affect the peak electricity load? Evidence from China

Cited by 18 publications

References 37 publications

The Impact of China's Paired Assistance Policy on the COVID-19 Crisis—An Empirical Case Study of Hubei Province

The Impact of China's Paired Assistance Policy on the COVID-19 Crisis—An Empirical Case Study of Hubei Province

Measurement of the Rebound Effect on Urban Household Energy Consumption Savings

Towards the prospect of carbon-neutral power system 2060: A Power-Meteorology-Society systematic view

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