Abstract
In order to find the model of rural energy transformation in Henan Province. In this paper, Tapio decoupling model is employed to investigate the pivotal factors affecting rural power consumption (PC) and total energy consumption (TEC) in Henan Province. In addition, PSO-BP is used to predict the values of each influencing factor in 2020–2025. Last, the STIRPAT model is used to forecast TEC and PC from 2020 to 2025 based on the data of rural energy consumption in Henan Province from 2009–2019. The results show that other factors besides population promote TEC and PC to different degrees. Moreover, the influencing factors, TEC and PC, form a virtuous cycle of mutual promotion. Then, TEC and PC consumption show an increasing trend year by year in 2020–2025. It is worth noting that after 2022, the variation of PC is greater than that of TEC. To sum up, improving rural electrification level is a necessary way to realize its low-carbon energy transition.
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References
Agrawal S, Harish SP, Mahajan A et al (2020) Influence of improved supply on household electricity consumption - Evidence from rural India. Energy 211:118544. https://doi.org/10.1016/j.energy.2020.118544
Ali G, Anbren S, Bashir MK (2018) Climate mitigation, low-carbon society, and dynamism of educational institutes in a low-income country. Environ Sci Pollut Res 25:3775–3784. https://doi.org/10.1007/s11356-017-0607-9
Amagai K, Takarada T, Funatsu M, Nezu K (2014) Development of low-CO2-emission vehicles and utilization of local renewable energy for the vitalization of rural areas in Japan. IATSS Research 37:81–88. https://doi.org/10.1016/j.iatssr.2013.11.001
Asmare F, Jaraitė J, Kažukauskas A (2021) The effect of descriptive information provision on electricity consumption: Experimental evidence from Lithuania. Energy Econ 104:105687. https://doi.org/10.1016/j.eneco.2021.105687
Bashir MK, Schilizzi S, Sadler R, Ali G (2018) Vulnerability to food insecurity in rural Punjab, Pakistan. British Food J 120:2088–2101. https://doi.org/10.1108/BFJ-10-2017-0597
Du Q, Han X, Li Y et al (2021) The energy rebound effect of residential buildings: Evidence from urban and rural areas in China. Energy Policy 153:112235. https://doi.org/10.1016/j.enpol.2021.112235
Duan T, Feng J, Zhou Y et al (2021) Systematic evaluation of management measure effects on the water environment based on the DPSIR-Tapio decoupling model: A case study in the Chaohu Lake watershed China. Sci Total Environ 801:149528. https://doi.org/10.1016/j.scitotenv.2021.149528
Gani A (2021) Fossil fuel energy and environmental performance in an extended STIRPAT model. J Clean Prod 297:126526. https://doi.org/10.1016/j.jclepro.2021.126526
Ghazali A, Ali G (2019) Investigation of key contributors of CO2 emissions in extended STIRPAT model for newly industrialized countries: A dynamic common correlated estimator (DCCE) approach. Energy Rep 5:242–252. https://doi.org/10.1016/j.egyr.2019.02.006
Gong Q, Guo G, Li S, Liang X (2021) Decoupling of urban economic growth and water consumption in Chongqing and Chengdu from the “production-living-ecological” perspective. Sustain Cities Soc 75:103395. https://doi.org/10.1016/j.scs.2021.103395
Han J, Zhang L, Li Y (2021) Spatiotemporal analysis of rural energy transition and upgrading in developing countries: The case of China. Appl Energy, 118225.https://doi.org/10.1016/j.apenergy.2021.118225
Han P, Kimura F, Sandu S (2020) Household-level analysis of the impacts of electricity consumption on welfare and the environment in Cambodia: Empirical evidence and policy implications. Econ Model 89:476–483. https://doi.org/10.1016/j.econmod.2019.11.025
He B-J, Yang L, Ye M (2014) Building energy efficiency in China rural areas: Situation, drawbacks, challenges, corresponding measures and policies. Sustain Cities Soc 11:7–15. https://doi.org/10.1016/j.scs.2013.11.005
He S, Chen W, Mu X, Cui W (2020) Constrained optimization model of the volume of initial rainwater storage tank based on ANN and PSO. Environ Sci Pollut Res 27:21057–21070. https://doi.org/10.1007/s11356-020-08630-6
Huo T, Li X, Cai W et al (2020) Exploring the impact of urbanization on urban building carbon emissions in China: Evidence from a provincial panel data model. Sustain Cities Soc 56:102068. https://doi.org/10.1016/j.scs.2020.102068
Hussain J, Pan Y, Ali G, Xiaofang Y (2020) Pricing behavior of monopoly market with the implementation of green technology decision under emission reduction subsidy policy. Sci Total Environ 709:136110. https://doi.org/10.1016/j.scitotenv.2019.136110
Imran M, Ozcatalbas O, Bakhsh K (2019) Rural household preferences for cleaner energy sources in Pakistan. Environ Sci Pollut Res 26:22783–22793. https://doi.org/10.1007/s11356-019-05588-y
Jia P, Zhuang J, Vaca Lucero AM, Li J (2022) Does the energy consumption revolution improve the health of elderly adults in rural areas? Evidence from China. Sci Total Environ 807:150755. https://doi.org/10.1016/j.scitotenv.2021.150755
Jin J, He R, Kuang F et al (2019) Different sources of rural household energy consumption and influencing factors in Dazu, China. Environ Sci Pollut Res 26:21312–21320. https://doi.org/10.1007/s11356-019-05439-w
Keshtkarbanaeemoghadam A, Dehghanbanadaki A, Kaboli MH (2018) Estimation and optimization of heating energy demand of a mountain shelter by soft computing techniques. Sustain Cities Soc 41:728–748. https://doi.org/10.1016/j.scs.2018.06.008
Kim MK, Kim Y-S, Srebric J (2020) Impact of correlation of plug load data, occupancy rates and local weather conditions on electricity consumption in a building using four back-propagation neural network models. Sustain Cities Soc 62:102321. https://doi.org/10.1016/j.scs.2020.102321
Lemence ALG, Tamayao M-AM (2021) Energy consumption profile estimation and benefits of hybrid solar energy system adoption for rural health units in the Philippines. Renew Energy 178:651–668. https://doi.org/10.1016/j.renene.2021.06.090
Li H, Mu H, Zhang M, Li N (2011) Analysis on influence factors of China’s CO2 emissions based on Path–STIRPAT model. Energy Policy 39:6906–6911. https://doi.org/10.1016/j.enpol.2011.08.056
Lin B, Li Z (2020) Spatial analysis of mainland cities’ carbon emissions of and around Guangdong-Hong Kong-Macao Greater Bay area. Sustain Cities Soc 61:102299. https://doi.org/10.1016/j.scs.2020.102299
Lin S, Wang S, Marinova D et al (2017) Impacts of urbanization and real economic development on CO2 emissions in non-high income countries: Empirical research based on the extended STIRPAT model. J Clean Prod 166:952–966. https://doi.org/10.1016/j.jclepro.2017.08.107
Ma B, Yu Y, Urban F (2018) Green transition of energy systems in rural China: National survey evidence of households’ discrete choices on water heaters. Energy Policy 113:559–570. https://doi.org/10.1016/j.enpol.2017.11.046
Ma S, Xu X, Li C et al (2021a) Energy consumption inequality decrease with energy consumption increase: Evidence from rural China at micro scale. Energy Policy 159:112638. https://doi.org/10.1016/j.enpol.2021.112638
Ma X, Wang M, Chen D, Li C (2021b) Energy choice in rural household cooking and heating: influencing factors and transformation patterns. Environ Sci Pollut Res 28:36727–36741. https://doi.org/10.1007/s11356-021-13213-0
Mahajan A, Harish SP, Urpelainen J (2020) The behavioral impact of basic energy access: A randomized controlled trial with solar lanterns in rural India. Energy Sustain Dev 57:214–225. https://doi.org/10.1016/j.esd.2020.04.005
Malik A, Tikhamarine Y, Sammen SS et al (2021) Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms. Environ Sci Pollut Res 28:39139–39158. https://doi.org/10.1007/s11356-021-13445-0
Minaei F, Minaei M, Kougias I et al (2021) Rural electrification in protected areas: A spatial assessment of solar photovoltaic suitability using the fuzzy best worst method. Renew Energy 176:334–345. https://doi.org/10.1016/j.renene.2021.05.087
Peng H, Xiao Z, Wang J, Li J (2021) A decision support framework for new energy selection in rural areas from the perspectives of information reliability and criterion non-compensation. Energy 235:121435. https://doi.org/10.1016/j.energy.2021.121435
Rahman MdM, Paatero JV, Poudyal A, Lahdelma R (2013) Driving and hindering factors for rural electrification in developing countries: Lessons from Bangladesh. Energy Policy 61:840–851. https://doi.org/10.1016/j.enpol.2013.06.100
Riva F, Colombo E (2020) System-dynamics modelling of the electricity-development nexus in rural electrification based on a Tanzanian case study. Energy Sustain Dev 56:128–143. https://doi.org/10.1016/j.esd.2020.04.001
Robert FC, Gopalan S (2018) Low cost, highly reliable rural electrification through a combination of grid extension and local renewable energy generation. Sustain Cities Soc 42:344–354. https://doi.org/10.1016/j.scs.2018.02.010
Rose S, Ashfaq M, Hassan S, Ali G (2018) A Nexus between Malaria and Agricultural Output through the Channels of Gender, Sanitation, and Socio-Economic Status. Pol J Environ Stud 27:287–296. https://doi.org/10.15244/pjoes/71091
Sedai AK, Nepal R, Jamasb T (2021) Flickering lifelines: Electrification and household welfare in India. Energy Economics 94:104975. https://doi.org/10.1016/j.eneco.2020.104975
Shahbaz M, Chaudhary AR, Ozturk I (2017) Does urbanization cause increasing energy demand in Pakistan? Empirical evidence from STIRPAT model. Energy 122:83–93. https://doi.org/10.1016/j.energy.2017.01.080
Su B, Goh T, Ang BW, Ng TS (2022) Energy consumption and energy efficiency trends in Singapore: The case of a meticulously planned city. Energy Policy 161:112732. https://doi.org/10.1016/j.enpol.2021.112732
Suman GK, Guerrero JM, Roy OP (2021) Optimisation of solar/wind/bio-generator/diesel/battery based microgrids for rural areas: A PSO-GWO approach. Sustain Cities Soc 67:102723. https://doi.org/10.1016/j.scs.2021.102723
Tesfamichael M, Bastille C, Leach M (2020) Eager to connect, cautious to consume: An integrated view of the drivers and motivations for electricity consumption among rural households in Kenya. Energy Res Soc Sci 63:101394. https://doi.org/10.1016/j.erss.2019.101394
Vinicius GTF, Silvia C, Aleksandar D et al (2021) Rural electrification planning based on graph theory and geospatial data: A realistic topology oriented approach. Sustain Energy, Grids and Netw 28:100525. https://doi.org/10.1016/j.segan.2021.100525
Wang P, Wu W, Zhu B, Wei Y (2013) Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China. Appl Energy 106:65–71. https://doi.org/10.1016/j.apenergy.2013.01.036
Wen L, Yuan X (2020a) Forecasting the annual household electricity consumption of Chinese residents using the DPSO-BP prediction model. Environ Sci Pollut Res 27:22014–22032. https://doi.org/10.1007/s11356-020-08418-8
Wen L, Yuan X (2020b) Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO. Sci Total Environ 718:137194. https://doi.org/10.1016/j.scitotenv.2020.137194
Wenbo G, Yan C (2018) Assessing the efficiency of China’s environmental regulation on carbon emissions based on Tapio decoupling models and GMM models. Energy Rep 4:713–723. https://doi.org/10.1016/j.egyr.2018.10.007
Wu F, Liu X, Wang W et al (2015) Characterization of particulate-bound PAHs in rural households using different types of domestic energy in Henan Province, China. Sci Total Environ 536:840–846. https://doi.org/10.1016/j.scitotenv.2015.07.101
Wu Q (2021) Optimization of AI-driven communication systems for green hospitals in sustainable cities. Sustain Cities Soc 72:103050. https://doi.org/10.1016/j.scs.2021.103050
Wu R, Wang J, Wang S, Feng K (2021) The drivers of declining CO2 emissions trends in developed nations using an extended STIRPAT model: A historical and prospective analysis. Renew Sustain Energy Rev 149:111328. https://doi.org/10.1016/j.rser.2021.111328
Wu S (2020) The evolution of rural energy policies in China: A review. Renew Sustain Energy Rev 119:109584. https://doi.org/10.1016/j.rser.2019.109584
Yahyaoui I, Yahyaoui A, Chaabene M, Tadeo F (2016) Energy management for a stand-alone photovoltaic-wind system suitable for rural electrification. Sustain Cities Soc 25:90–101. https://doi.org/10.1016/j.scs.2015.12.002
Yang S, Cao D, Lo K (2018) Analyzing and optimizing the impact of economic restructuring on Shanghai’s carbon emissions using STIRPAT and NSGA-II. Sustain Cities Soc 40:44–53. https://doi.org/10.1016/j.scs.2018.03.030
Zhao L, Zhao T, Yuan R (2021) Scenario simulations for the peak of provincial household CO2 emissions in China based on the STIRPAT model. Sci Total Environ, 151098.https://doi.org/10.1016/j.scitotenv.2021.151098
Zi C, Qian M, Baozhong G (2021) The consumption patterns and determining factors of rural household energy: A case study of Henan Province in China. Renew Sustain Energy Rev 146:111142. https://doi.org/10.1016/j.rser.2021.111142
Zou B, Luo B (2019) Rural household energy consumption characteristics and determinants in China. Energy 182:814–823. https://doi.org/10.1016/j.energy.2019.06.048
Zou B, Mishra AK (2020) Appliance usage and choice of energy-efficient appliances: Evidence from rural Chinese households. Energy Policy 146:111800. https://doi.org/10.1016/j.enpol.2020.111800
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All authors contributed to the study conception and design. Material preparation, Data collection and analysis were carried out by Lei Wen and Qianqian Song. The first draft of the manuscript was written by Qianqian Song and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Appendix
Appendix
The appendix table.
Nomenclature | Definition | Units |
|---|---|---|
TEC | Total rural energy consumption in Henan Province | 10E 4▪tons |
PAM | Total power of agricultural machinery | 10E4▪kw |
EIA | Effective irrigation area | 10E3▪ha |
EE | Energy intensity | - |
POP | Rural population | 10E4 |
V | Total value of agricultural output | 10E8▪RMB |
PS | Per capita housing area | sq.m |
PC | Rural power consumption in Henan Province | 10E8 ▪kw ▪h |
EEP | Electric motor power | 10E4▪kw ▪h |
PI | Per capita income | RMB |
TP | Total power of rural durable goods | W/ 100 households |
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Wen, L., Song, Q. The forecasting model research of rural energy transformation in Henan Province based on STIRPAT model. Environ Sci Pollut Res 29, 75550–75565 (2022). https://doi.org/10.1007/s11356-022-21119-8
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DOI: https://doi.org/10.1007/s11356-022-21119-8