The impact of climate change on residential energy consumption in urban and rural divided southern and northern China

Fan, Jing-Li; Zeng, Bin; Jia, Hu; Zhang, Xian; Wang, Hang

doi:10.1007/s10653-019-00430-3

Cited by 21 publications

(7 citation statements)

References 43 publications

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“…With the increase in income, households will turn to cleaner cooking fuel. The distance from the town center will significantly reduce the probability of farmers' CCE use-this is consistent with Fan et al's [22] research that, due to geographical barriers, the use of clean energy for cooking by peasant households is limited; in addition, living in the suburbs of big cities will significantly increase the probability of CCE, and villages with plains or hills will have a higher probability of CCE use than in mountainous villages.…”

Section: Model Resultssupporting

confidence: 85%

“…Among the available studies, there are more studies on clean energy for farmers' cooking in the academic community. First, basic household characteristics, including household size, age, gender, and education level, etc., have been proven to have significant impacts on the energy consumption needs and choices of farmers [22][23][24][25][26][27][28], followed by household economic characteristics, particularly household income. For example, some studies found that household income can significantly promote the conversion of household cooking energy [29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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Labor Off-Farm Employment and Farmers’ Cooking Clean Energy Use: Evidence from Rural China

et al. 2022

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(Motivation) With the transfer of labor force and the continuous improvement of household income, the household energy consumption structure is also changing. (Gaps) However, few studies have explored the correlations between labor off-farm employment (LOE) and farmers’ cooking clean energy (CCE) consumption. (Methodologies) Using survey data of 8198 farmers in 27 provinces from China’s labor dynamic survey in 2016, the IV-Probit model was used to analyze the impact of LOE on CCE use, and the mediation effect model was used to examine the specific mechanism of action between them. (Results) The results found that: (1) LOE accounted for about 39%, and farmers’ utilization rate of CCE accounted for 40%. (2) LOE has significantly promoted the use of CCE by farmers, and this action mechanism is mainly realized through three paths, that is, per capita annual cash income, social relationship network, and family population structure. (3) The heterogeneity analysis results showed that the head of the household having a university education or above, the family being located in rural areas and mountainous areas, and LOE will have a greater positive impact on the farmers’ CCE use. (Policy) From the micro perspective of LOE, this study can deepen our understanding of LOE and CCE use decisions, and then provide a reference for the rational allocation of labor resources and farmers’ CCE-related policy formulation.

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Section: Model Resultssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Labor Off-Farm Employment and Farmers’ Cooking Clean Energy Use: Evidence from Rural China

et al. 2022

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“…Terefore, as the province's urbanization rate has reached 72%, its impact on REC is expected to gradually decrease in the future. Per capita housing construction area that has a signifcant positive impact on REC is also found in other studies [43,85]. Tis has a signifcant impact on lighting and air conditioning [53,86], and together with the number of residents, has a signifcant impact on the number and use intensity of household appliances (e.g., lighting and air conditioning) [87], leading to more energy being consumed to meet work needs [53].…”

Section: Analysis Of the Impact Factorssupporting

confidence: 61%

“…Based on the literature review and understanding of the knowledge of the REC impact factors (see Table 1), cooling degree days (CDD) and heating degree days (HDD) are added to the original three dimensions of the STIRPAT model [40][41][42][43] to give the following equation:…”

Section: Te Stirpat Modelmentioning

confidence: 99%

Strategy of Energy Conservation and Emission Reduction in Residential Building Sector: A Case Study of Jiangsu Province, China

Jiang

Peng

et al. 2023

Journal of Environmental and Public Health

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The building sector is the second-largest energy consumer in China. With the proposal to reach a carbon peak by 2030 and achieve carbon neutrality by 2060, China attaches more importance to the energy conservation and emission reduction of the residential sector. To study the connection between socioeconomic factors and residential energy consumption (REC), this paper collects the data of 13 prefecture-level cities in Jiangsu Province, China, from 2001 to 2019 to explore the REC impact factors by the STIRPAT model. The factors for modeling are identified from relevant studies and weighted by the independent weight coefficient method (IWCM). The regression result shows that the average number of persons per household, per capita housing construction area, urbanization rate, and cooling degree days have a significant positive impact on REC, while a negative correlation is found between per capita housing construction area, residential water consumption, and residential liquefied petroleum gas (LPG) consumption. Strategies of energy conservation and emission reduction in residential building sector are explored based on the demonstration of the future REC pattern evolution and the changes in its impact factors.

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“…Third, fiscal subsidy is an important driving force for the rapid development of China’s PV industry (Fan et al 2020 ). Based on the division of three types of subsidy coverage areas based on the local light intensity, the fiscal subsidy for China’s PV industry provides 20 years’ funds support for clean energy according to their actual PV power generation (Lin and Luan 2020 ).…”

Section: The Framework Of Pv Industry’s Cost System and Sub-system Dementioning

confidence: 99%

The influence of COVID-19 on grid parity of China’s photovoltaic industry

Song

Liu

et al. 2020

Environ Geochem Health

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With the more efficient involvement of both technology and policy factors in China's whole industry-chain, the year 2020 is a key period for photovoltaic (PV) industry to achieve grid parity. In this context, COVID-19 may trigger a certain timedelay in new installed PV projects, thereby bringing an uncertain influence on the whole PV industry. To forecast the influence degree and influence cycle of COVID-19 on PV industry, this paper firstly clarifies the key features of epidemic situation as well as the basic rule of such pandemics' transmission along industry-chain. Then this paper constructs a system dynamics model targeting at cost accounting of PV power generation under the influence of COVID-19 and thus forecasts the variation rules, superposition effects and influence cycle of levelized cost of energy (LCOE) of PV power generation and the operations cost of each subsystem. Empirical results show that PV industry has a lag response to the COVID-19 for 1 quarter and periodic response for 4 quarters, which is mainly embodied in the rise of short-term production cost. At the same time, the influence of COVID-19 on the upstream firms of PV industry is stronger than that on downstream firms. With the gradual recovery of whole industry-chain, LCOE of PV power generation will rapidly return to the previous expected level of grid parity by the end of 2020.

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The impact of climate change on residential energy consumption in urban and rural divided southern and northern China

Cited by 21 publications

References 43 publications

Labor Off-Farm Employment and Farmers’ Cooking Clean Energy Use: Evidence from Rural China

Labor Off-Farm Employment and Farmers’ Cooking Clean Energy Use: Evidence from Rural China

Strategy of Energy Conservation and Emission Reduction in Residential Building Sector: A Case Study of Jiangsu Province, China

The influence of COVID-19 on grid parity of China’s photovoltaic industry

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