The forecasting model research of rural energy transformation in Henan Province based on STIRPAT model

The forecasting model research of rural energy transformation in Henan Province based on STIRPAT model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The forecasting model research of rural energy transformation in Henan Province based on STIRPAT model Lei Wen, Qianqian Song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1203734/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In order to find the model of rural energy transformation in Henan Province. This paper examines the effects of population, per capita income, total value of agricultural output, per capita housing area, electric motor power, total power of rural durable goods on rural power consumption (PC) and effective irrigation area, per capita housing area, total power of agricultural machinery, total value of agricultural output, rural population, and energy intensity on rural total energy consumption (TEC) employing Tapio decoupling model. In addition, PSO-BOP 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 2020-2025 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. Rural power consumption Rural power consumption Rural energy transformation Henan Province STIRPAT model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Energy is an important material foundation for the survival and development of human society. It is vital to the national economy and people's livelihood and the strategic competitiveness of a country. In today's world, the massive use of fossil energy brings about a series of problems in the fields of environment, ecology and global climate change. Countries around the world have taken the initiative to solve the dilemma and accelerate energy transformation and development. Yet now, around 2.8 billion people worldwide still rely on traditional biomass for cooking, heating and so on, 95% of which are concentrated in rural areas in Asia and Africa (Ma et al. 2021b ; Han et al. 2021 ). China is a big agricultural country with vast rural area and the rural population. About half of China's population lives in the countryside. Since the reform and opening up, the traditional rural energy consumption structure has also undergone significant changes (Jin et al. 2019 ). Nonetheless, China's combined agricultural and rural greenhouse gas emissions account for about 15 percent of national emissions (Han et al. 2021 ). Promoting rural energy revolution and realizing rural energy transformation and upgrading are crucial measures to implement the spirit of the 19th CPC National Congress and General Secretary Xi Jinping's strategic thought on energy revolution. Therefore, it is an effective way to realize the green development of rural areas to explore the path of low-carbon energy transition in China. Henan is the largest agricultural province in central China, with a population of 96.4 million. Residents of Henan Province are heavily concentrated in the countryside. As an important province with a large population and large grain and agricultural production, Henan is the main battlefield of the rural revitalization strategy (Wu et al. 2015 ; Zi et al. 2021 ). In January 2017, the People's Government of Henan Province issued the 13th Five-Year Energy Development Plan of Henan Province, which put forward eight key tasks, including accelerating the development of non-fossil energy, strengthening rural energy construction and building a smart energy system. The supply-side structural reform in the energy sector has achieved remarkable results. The average annual concentration of PM2.5 and PM10 has dropped by more than 30%, and all environmental indicators have reached the best level in the past five years. The 14th Five-Year Plan points out that it is necessary to build a low-carbon and efficient rural energy support system in Henan Province, continue to promote the energy revolution, and actively develop new and renewable energy. In addition, the strategic Plan for Rural Revitalization of Henan Province (2018-2022) specifies the key tasks of "promoting the rural energy revolution". It is mainly reflected in optimizing the rural energy supply structure and constructing a clean, low-carbon, safe and efficient modern rural energy system. Therefore, exploring the driving factors of rural energy consumption in Henan Province and predicting the trend of total energy consumption (TEC) and power consumption (PC) in Henan Province are crucial steps to explore the mode of rural energy transformation and upgrading in Henan Province. For these reasons, the main tasks of this paper are to analyze the current situation of rural energy consumption in Henan Province, explore the driving factors affecting TEC and PC in rural areas of Henan Province, and forecast TEC and PC to analyze their changing trend. So as to explore the low-carbon energy transformation model of Henan Province. In addition, the technical roadmap for this article is shown in Fig. 1 . The contributions and originalities of this paper can be summarized as follows: (1) In this paper, the decoupling states between rural PC and TEC and its influencing factors in Henan Province are analyzed by using decoupling principle creatively. (2) Since there is multicollinearity among the 6 influencing factors of TEC, and the same problem exists among the 6 influencing factors of PC. Therefore, based on STIRPAT model, this paper uses ridge regression method to fit the linear equations of TEC and PC originally, and the validity of the fitted equations are verified. Rather than simply summing up the primary energy consumption of rural Henan Province. (3) In addition, this paper optimizes the BP neural network by PSO, and innovatively predicts the values of influencing factors of PC and TEC. (4) Finally, the consumption of TEC and PC in 2020-2025 are obtained by STIRPAT using ridge regression. Also, the forecast results of TEC and PC are analyzed to find the mode of rural energy transformation in Henan Province. The rest of the article is structured as follows: Section 2 provides a relevant literature review for rural energy consumption, rural power consumption and the application of STIRPAT model. Variable selection for total energy consumption and power consumption of Henan Province are introduced based on Tapio decoupling model in Section 3. Theoretical model construction and data in Section 4 . Section 5 analyzes and discusses the results of regression equations. Section 6 provides the conclusions and policy recommendations. 2. Literature Review 2.1. Research on rural energy consumption In China's rural areas, energy consumption is mainly used for villagers' life, such as heating, cooking, lighting, transportation and agricultural production activities, such as irrigation, planting, harvesting and fertilization (Ma et al. 2021a ). Due to China's rapid urbanization process, energy consumption in rural areas is increasing. Moreover, a large amount of CO2 gas is emitted, which poses a threat to the health of villagers (Amagai et al. 2014 ). Technological advances in energy efficiency and replacing conventional energy with renewable energy are generally regarded as effective measures to solve the problem of harmful gas emissions (Wu 2020 ; Du et al. 2021 ; Su et al. 2022 ).However, the consumption of fossil fuels supports the total consumption of rural energy, and the use of clean energy is less. Consequently, many scholars have explored the transformation mode of rural energy. Han et al.(Han et al. 2021 ) used the space-time model to study the space-time characteristics of energy ladder in agriculture and rural life of developing countries, taking China as an example, by analyzing rural life and production activities. From the perspective of information reliability and standard non-compensation, Peng et al.(Peng et al. 2021 ) constructed a decision support framework for managing the choice of new energy in rural areas. This is of great significance to the realization of the national low-carbon sustainable goal. He et al.(He et al. 2014 ) proposed to reduce the total energy consumption in rural areas by improving the energy efficiency of rural buildings. Jia et al.(Jia et al. 2022 ) analyzed the impact of the energy consumption revolution on the health of the elderly in rural areas and its mechanism. And put forward, should improve the rural medical security level. So as to improve the physical and mental health of the elderly to ensure the successful realization of the rural energy revolution. Yahyaoui et al. (Yahyaoui et al. 2016 ) explored the installation scheme of renewable clean energy power generation devices in remote rural areas. It provides important policy suggestions for the green and sustainable development of rural areas. Ma et al. (Ma et al. 2021a ) analyzed the current situation of energy consumption in rural China, obtained the main factors affecting rural energy consumption, identified the key factors to improve energy efficiency, and provided a meaningful reference for rural low-carbon energy transformation. Lemence and Tamayao (Lemence and Tamayao 2021 ) identified hybrid solar systems as a powerful solution for sustainable energy in rural Philippines, and developed a grid connection solution with the lowest cost for solar photovoltaic. Zou and Luo (Zou and Luo 2019 ) used Tobit model to estimate the determinants of energy consumption of rural households in China, based on the data of 1,472 rural households in a comprehensive survey. The results show that households with higher economic and educational levels have higher levels of electrification of energy consumption (Zou and Luo 2019 ; Li et al. 2021 ). Imran et al. (Imran et al. 2019 ) collected primary data from 196 households in four districts of Pakistan's Punjab province to explore the impact of biomass energy on human health in rural areas. Zi et al. (Zi et al. 2021 ) analyzed the cooking energy mode in rural areas of Henan Province and learned that the low-carbon transformation of cooking energy is of great help to the transformation of rural energy. Thus, only by analyzing the current situation of rural energy consumption can we find a way out of rural energy transformation. However, most of the above literature is cross-regional analysis of the path of national rural energy transformation. Hence, this paper focuses on the analysis of the current situation of rural energy in Henan Province. 2.2. Research on rural power consumption Studies on power consumption in rural areas have found that, with the improvement of rural living standards, energy consumption increases and electricity consumption also shows a rising trend (Tesfamichael et al. 2020 ; Agrawal et al. 2020 ). The increase of per capita income makes the transformation of rural energy use to commercialization (Asmare et al. 2021 ). That is, the villagers will gradually shift their dependence on non-commercial energy sources such as straw, firewood and bulk coal to electricity (Riva and Colombo 2020 ). The improvement of rural electrification can not only improve the efficiency of people's work and study, but also diversify people's ways of entertainment and study, thus improving people's physical and mental health (Han et al. 2020 ). Electrification has also increased the use of household appliances in rural areas and improved the quality of life of the people (Mahajan et al. 2020 ), thereby changing lifestyles, improving the per capita income, and accelerating the pace of urbanization in rural areas towards a relatively well-off society (Sedai et al. 2021 ). Yet 1.2 billion people still do not use electricity, and most of them live in rural areas (Robert and Gopalan 2018 ; Vinicius et al. 2021 ). Therefore, the driving factors for improving rural electrification level should be explored while improving grid coverage. Ma et al. proposed that the purchase rate of energy-saving household appliances is greatly affected by the education level of rural households, per capita income and government subsidies (Ma et al. 2018 ), as well as the influence of place and custom (Zou and Mishra 2020 ). (Rahman et al. 2013 ) analyzed that the success of rural electrification program in Bangladesh is significantly related to the driving factors of system investment, community participation, anti-corruption characteristics, standardized practices and performance incentives. It provides a meaningful reference for rural electrification in other countries. Minaei et al. (Minaei et al. 2021 ) proposed to improve rural electrification by improving the quality of life in rural and remote areas through clean electricity. Nonetheless, these studies only focus on the influencing factors of rural electrification from a single perspective. Correspondingly, the task of this paper is to explore the driving factors affecting rural electricity consumption from various aspects. 2.3. Research on STIRPAT STIRPAT model has been successfully applied to study the environmental pollution caused by various factors. Based on STIRPAT model, the scenario of peak household carbon dioxide emissions at provincial level in China was simulated by Zhao et al.(Zhao et al. 2021 ). Wu et al., Ghazali and Ali (Ghazali and Ali 2019 ; Wu et al. 2021 , p. 2) used the extended STIRPAT model to analyze the drivers of the downward trend of CO2 emissions in developed countries. Using the extended STIRPAT model, Lin et al.(Lin et al. 2017 ) explores the impact of urbanization and real economy development on co2 emissions in developing countries. Also, some literatures also use STIRPAT model to explore the driving factors of energy consumption. Gani (Gani 2021 ) researched the impact of fossil fuel power generation on environmental quality, based on a STIRPAT model that incorporated economic, social and institutional factors. Using STIRPAT model, Shahbaz et al. (Shahbaz et al. 2017 ) re- investigated the relationship between urbanization and energy consumption in Pakistan during 1972Q1-2011Q4. And it confirmed that urbanization increases energy consumption. Employing the extended STIRPAT model, Wang et al. (Wang et al. 2013 ) inspected the effects of population, economic level, technological level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade level on energy consumption in Guangdong from 1980 to 2010. The empirical results show that population, urbanization level, per capita GDP, industrialization level and service level can lead to the increase of total energy consumption, and then increase CO2 emissions. Whereas, there is no literature to assess the main drivers of rural energy in China applying STIRPAT model. Therefore, the primary purpose of this paper is to investigate the driving factors affecting rural energy consumption in Henan Province based on STIRPAT model. 3. Analysis Of The Current Situation Of Rural Energy In Henan Province This section introduces the decoupling principle to analyze the influencing factors of rural energy consumption and electricity consumption in Henan Province. 3.1. Variable selection 3.1.1. The decoupling theory The word "decoupling" originated in physics. Decoupling means that environmental pressure and economic growth are no longer interdependent, and changes in environmental pressure indicators such as carbon emissions are no longer synchronized with changes in economic development, and do not affect each other (Gong et al. 2021 ). The Tapio decoupling model analyzes the decoupling relationship between variables through the concept of elasticity. Tapio decoupling model is not only not affected by the change of statistical dimension, but also can decompose the causal chain of the decoupling index by using the identity, so as to realize the in-depth analysis and research on the influencing factors behind the decoupling state (Duan et al. 2021 ). This paper studies the decoupling relationship between rural TEC and its influencing factors in Henan Province and the decoupling relationship between rural PC and its influencing factors. The expression is as follows: $$D=\frac{\varDelta Q/Q}{\varDelta Yi/Yi}$$ 1 where, D is decoupling elasticity index; Q is rural TEC or EC; Y is the influencing factor. Consequently, the equation of energy influencing factors based on decoupling theory can be written as: $$D=\frac{\varDelta E/E}{\varDelta Wi/Wi}$$ 2 where, \(Wi\) represents the ownership of various factors affecting rural TEC in Henan Province; \(\varDelta Wi\) represents its increment; E and \(\varDelta E\) represent the rural TEC and its increment in Henan Province in the current year. Consequently, the equation of electricity influencing factors based on decoupling theory can be written as: $$D=\frac{\varDelta C/C}{\varDelta Ni/Ni}$$ 3 where, Ni represents the ownership of various factors affecting rural PC in Henan Province; \(\varDelta Ni\) represents its increment; C and \(\varDelta C\) represent the rural PC and its increment in Henan Province in the current year. Tapio decoupling indicators take the positive and negative values of \(\varDelta Q\) and \(\varDelta Y\) , as well as the elastic values of 0.8 and 1.2 as critical values, respectively, to judge the state and degree of decoupling. Eight decoupling states are defined according to the value of decoupling elasticity (Wenbo and Yan 2018 ), as shown in Table 1 . Table 1 Classification of Tapio decoupling states. Decoupling state Decoupling type ΔQ \(\varDelta Yi\) Elastic coefficient D Negative decoupling Negative decoupling of growth (NDG) >0 >0 D>1.2 Strong negative decoupling (SND) >0 >0 D0 >0 0<D0 >0 0.8<D0 >0 0.8<D0 >0 0<D0 >0 D0 >0 D>1.2 3.1.2. The variable selection In this paper, EIA, PS, PAM, V, POP and EE are taken as the influencing factors of TEC in Henan Province. Based on the data of TEC and influencing factors in rural areas of Henan Province from 2009 to 2019, the decoupling analysis is conducted based on equation ( 2 ). The results are shown in Table 2 and Fig. 2 . Table 2 Decoupling states between TEC and influencing factors. Time EIA PS PAM V POP EE 2009-2010 NDG NDG GC WD SND SND 2009-2011 NDG NDG NDG WD SND SND 2009-2012 NDG NDG NDG WD SND SND 2009-2013 NDG NDG GC WD SND SND 2009-2014 NDG NDG GC WD SND SND 2009-2015 NDG NDG NDG WD SND SND 2009-2016 NDG GC NDG WD SND SND 2009-2017 NDG GC NDG WD SND SND 2009-2018 NDG NDG NDG WD SND SND 2009-2019 NDG NDG NDG WD SND SND According to the Table 2 , there is a negative decoupling of growth between EIA and total energy consumption TEC. The association between PAM and TEC is negative decoupling of growth and growing concatenate. Also, the relationship between PAM and TEC is negative decoupling of growth and growing concatenate. The relationship between V and TEC shows weak decoupling. POP and EE show strong negative decoupling relationship with TEC. As shown in Fig. 2 , EIA, PS, PAM, and V all show positive decoupling effects on TEC. Among them, the most influential factor is the EIA, which shows a trend of wave rise. In addition, the decoupling index of PS, PAM and V have approximately the same trend. However, POP and EE have a negative decoupling effect on TEC, and the decoupling elasticity index of POP is large. In terms of PC, V, EEP, POP, PI, PS, and TP are taken as the influencing factors of PC in Henan Province. Based on the data of PC and influencing factors in rural areas of Henan Province from 2009 to 2019, the decoupling analysis is conducted based on equation ( 3 ). The results are shown in Table 3 , and Fig. 3 . Table 3 Decoupling states between PC and influencing factors. Time V PI PS EEP POP TP 2009-2010 WD WD NDG NDG SND WD 2009-2011 WD WD NDG NDG SND WD 2009-2012 WD WD NDG NDG SND WD 2009-2013 WD WD NDG NDG SND WD 2009-2014 WD WD NDG NDG SND WD 2009-2015 WD WD NDG NDG SND WD 2009-2016 WD WD NDG NDG SND WD 2009-2017 WD WD NDG NDG SND WD 2009-2018 WD WD NDG NDG SND WD 2009-2019 WD WD NDG NDG SND WD The Table 3 shows that the variables V, PI and TP show weak decoupling with rural electricity consumption PC in Henan Province. In addition, the states of PS and EEP are negative decoupling with PC. However, there is a strong negative decoupling between POP and PC. As shown in Fig. 3 , the analysis of V, PI, PS, EEP and TP from 2009 to 2019 show a positive decoupling effect on PC. Among them, EEP has the most significant impact on PC, the second is the PS, and V, PI and TP have the same influence on PC. 4. Theoretical Model Construction And Data In this study, to fully investigate the characteristics of rural energy usage of Henan Province in total energy consumption (TEC) and power consumption (PC). Firstly, STIRPAT method is used to fit and forecast the total amount of rural energy consumption and electricity consumption in Henan Province, and the model is verified. The next, PSO- BP is involved to predict the change of the influencing factors of TEC and PC in Henan Province. 4.1. Theoretical model construction 4.1.1. The STIRPAT theory The STIRPAT model originated from IPAT(Impact by Population Affluence and Technology), first proposed by Ehrlich and Holdren (1971) (Yang et al. 2018 ; Gani 2021 ; Zhao et al. 2021 ).The IPAT identity (I = PAT) is often used as a basis for investigating the effects of various factors on environmental pollution. Hence, STIRPAT model has become an important tool for analyzing energy consumption and other types of pollution (Huo et al. 2020 ; Lin and Li 2020 ). I = PAT (4) where, I is the impact, which is usually measured by the emission level of pollutants; P is the size of the population; A is the wealth of a country; T is the technical index. In order to fully study the factors affecting environmental change, IPAT model is too simple and has its limitations. Therefore, using this model as the basis, Dietz and Rosa proposed the STIRPAT model as follows (Huo et al. 2020 ; Lin and Li 2020 ): $${I}_{t}=a{P}_{t}^{b}{A}_{t}^{c}{T}_{t}^{d}{\epsilon }_{t}$$ 5 where, a is the intercept term; P, A, and T are the same as in Equation (4); B, C and D represent the elasticity of environmental impact on P, A and T respectively. \({\epsilon }_{t}\) is a random disturbance, and the subscript t indicates the year. STIRPAT model has been widely used to analyze the factors affecting environmental pollution(Li et al., 2011 ; Wang et al., 2013 ).To eliminate possible heteroscedasticity, all variables are in logarithmic form. Thus, the equation can be rewritten as: \(Ln{I}_{t}\) = \({ln}a\) + \(b\left({ln}{p}_{t}\right)\) + \(c\left({ln}{A}_{t}\right)\) + \(d\left({ln}{T}_{t}\right)\) + \({\epsilon }_{t}\) (6) where, P represents population size (10E4 persons); A is per capita GDP (yuan); T is a technical indicator, measured by energy efficiency (energy consumption in ISI/its actual output − ENE). To further investigate the driving forces of TEC in rural areas of Henan Province. Based on the specific situation of Henan Province, total rural machinery power, effective irrigation area, energy intensity, rural population, total agricultural output value and per capita housing area of farmers are incorporated into STIRPAT model for improvement and expansion. The rewritten expression is: $$L{nTEC}_{t}=Lna+{\beta }_{1}LnPA{M}_{t}+{\beta }_{2}LnEI{A}_{t}+{\beta }_{3}LnE{E}_{t}+{\beta }_{4}LnP{OP}_{t}+{\beta }_{5}Ln{V}_{t}+{\beta }_{6}LnP{S}_{t}+{\xi }_{t}$$ 7 where, \({TEC}_{t}\) represents the rural energy consumption of Henan Province in t years (10E4▪ tons); PAM represents the total power of agricultural machinery; EIA stands for effective irrigation area; EE stands for energy intensity; POP stands for rural population; V represents the total value of agricultural output; PS represents per capita residential area; t is the year. In addition, total agricultural output value, electric motor power, rural population, per capita income, per capita housing area of farmers and total power of rural durable goods are incorporated into STIRPAT model for improvement and expansion. The econometric model and equation of rural PC in Henan Province is established: $$Ln{PC}_{t}=Lna+{\beta }_{1}Ln{V}_{t}+{\beta }_{2}LnEE{P}_{t}+{\beta }_{3}LnPO{P}_{t}+{\beta }_{4}LnP{I}_{t}+{\beta }_{5}LnP{S}_{t}+{\beta }_{6}LnT{P}_{t}+{\xi }_{t}$$ 8 where, P represents the rural PC of Henan Province (100 million kw▪h); V represents the total agricultural output value; EEP stands for electric motor power; POP stands for rural population; PI stands for per capita income; PS represents per capita housing area; TP represents the total power of rural durable goods. The relevant variables in Equations ( 7 ) and ( 8 ) are defined in Table Nomenclature. 4.1.2. The BP theory BP network (Back Propagation) was proposed by a group of scientists headed by Rumelhart and McCelland in 1986 (Wen and Yuan 2020a ; Kim et al. 2020 ). BP is a kind of multilayer feed-forward network trained by error backpropagation algorithm, which is one of the most widely used neural network models. The learning process of BP includes the forward propagation of information and the back propagation of error. After repeated training, the network weight and deviation changes are calculated continuously in the direction of relative error function gradient descent, gradually approaching the target. A typical three-layer BP neural network structure is shown in Fig. 4 (Keshtkarbanaeemoghadam et al. 2018 ). In the figure, \(\varSigma\) represents weighted sum; \(\int\) is the activation function. 4.1.3. The particle swarm optimization (PSO) theory Particle Swarm Optimization (PSO) was proposed by Dr. Eberhart and Dr. Kennedy in 1995 inspired by artificial life. It simulates the random foraging behavior of a flock of birds in space (Suman et al. 2021 ; Malik et al. 2021 ). Assuming that none of the birds know exactly where the food is, but they do know roughly how far it is, the simplest and most effective method is to search the area around the bird that is currently closest to the food. Hence PSO treats each bird as a particle with position and velocity. Then, the particle uses the closest location it has found to the food and the closest location the group has found so far to change its direction of flight. Finally, the whole population is directed to the same place, and that place is the area closest to the food. In the process of optimization, the velocity and position of each particle are updated according to the following formulas: $${V}_{i,d}^{T+1}=w{v}_{i,d}^{T}+{c}_{1}{r}_{1}\left({P}_{\dot{l}best}-{x}_{i}^{T},d\right)+{c}_{2}{r}_{2}\left({g}_{best,d}-{x}_{i,d}^{T}\right)$$ 9 $${x}_{i,d}^{T+1}={x}_{i,d}^{T}+{v}_{i,d}^{T+1}$$ 10 where, d=1, …,n; \({x}_{i}^{T}\) and \({v}_{i}^{T}\) represent the position and velocity of the ith particle in the T iteration respectively. c1 represents self-cognition coefficient; c2 represents social cognition coefficient; r1 and r2 are random numbers between [0,1]; \({P}_{\dot{l}best}\) represents the individual optimal position found by the ith particle until the T iteration; \({g}_{best}\) represents the global optimal position of the whole population until iteration T; \({x}_{i,d}^{T+1}\) and \({V}_{i,d}^{T+1}\) represent the position and velocity of the ith particle obtained after the T+1 iteration of the particle d, respectively. 4.1.4. The PSO-BP model Based on the introduction of BP and PSO above, this paper uses PSO to improve BP to predict the changes of various influencing factors (Wen and Yuan 2020b ; He et al. 2020 ; Wu 2021 ). The specific steps are as follows: Step 1: The fitness function of PSO is set as shown in formula (11). $$F\left(z\right)=e$$ 11 where e is the error function in formula (12): e = λMAE + µRMSE + ηMAPE (12) where λ and µ are non-negative weights. Also, the expressions of MAE and RMSE are shown below: MAE = \(\frac{1}{n}\sum _{i=1}^{n}\left|{a}_{i}-{\widehat{a}}_{i}\right|\) (13) RMSE = \(\frac{1}{n}\sum _{i=1}^{n}\frac{\left|{a}_{i}-{\widehat{a}}_{i}\right|}{{a}_{i}}\) (14) MAPE = \(\frac{1}{n}\sum _{\text{i}=1}^{\text{n}}({\text{a}}_{\text{i}}-{\widehat{\text{a}}}_{\text{i}})²\) (15) where, \({a}_{i}\) and \({\widehat{a}}_{i}\) are the actual value and predicted value respectively, and n is the number of samples. Step 2: Initialize the position and velocity of each particle, initialize parameters c1,c2, maximum velocity Vmax, and minimum velocity Vmin, population size N, and maximum iteration Tmax. Step3: The fitness function of each particle in the population is calculated, and the initial individual optimum and the total optimum are obtained. Step4: Update the velocity and position of each particle according to equations ( 9 ) and ( 10 ). Also, update individual and global optimality. Step5: If the iteration number is equal to Tmax, the algorithm ends. And output the optimal value and the optimal particle. Step 7: Otherwise, iter = iter+1, returning step 4. Step 8: The value of the optimal particle is taken as the weights and thresholds of BP. Step 9: Finally gets a trained network of BP. The flow chart of PSO-BP is shown in Fig. 5 . In the figure, E is the error of backpropagation, and ε is the number that is infinitely close to 0. (16) where, O is the output vector of the output layer; d is the expected output vector; x is the expected input vector; l is the dimension of the output vector of the output layer; \(w\) is the connection weight between the output layer and the hidden layer; v is the connection weight between the input layer and the hidden layer; m is the dimension of the hidden layer output vector. 4.2. Data Data were mainly drawn from government statistical yearbooks. The energy consumption and power consumption of Henan Province are from China Energy Statistical Yearbook (National Bureau of Statistics, 2020). The data of influencing factors total power of agricultural machinery, effective irrigation area, energy intensity, rural population, per capita housing area, total value of agricultural output, electric motor power, per capita income, and total power of rural durable goods are from the Statistical Yearbook of Henan Province (Henan Bureau of Statistics, 2020). 5. Results And Discussion 5.1. Recognition results of regression equation 5.1.1. Multicollinearity analysis The multicollinearity test is performed based on the data of each influencing factor and rural TEC and rural PC from 2009 to 2019. The multicollinearity test results of various influencing factors of rural TEC are shown in Table 4 and Table 5 . The multicollinearity test results of various influencing factors of rural PC are shown in Table 6 and Table 7 . Table 4 Correlation test results of factors of TEC. lnPAM lnEIA lnEE lnPOP lnV lnPS lnPAM 1 lnEIA 0.904** 1 lnEE 0.932** 0.769** 1 lnPOP 0.973** 0.921** 0.856** 1 lnV 0.992** 0.898** 0.927** 0.980** 1 lnPS 0.941** 0.897** 0.776** 0.981** 0.944** 1 Table 5 OLS regression results of factors of TEC. OLS result Unstandardized coefficient t-Statistic Sig. VIF Constant -13.214 0.105 - lnPAM -1.790 0.023 133.725 lnEIA 3.104 0.014 9.217 lnEE 0.582 0.009 34.595 lnPOP 0.122 0.610 125.312 lnV 0.218 0.486 132.827 lnPS 1.087 0.121 75.874 Adjusted R 2 0.973 - - F-statistic Sig. 0.000 - - According to the results shown in Table 4 , it is clear that more than half of the variables are correlated, among which the relationship values of total agricultural machinery power, energy efficiency, population, gross output value and housing area per capita are all high. In addition, VIF is the most common collinearity evaluation standard. The VIF values of agricultural machinery power, population, total agricultural output value and housing area per capita, energy efficiency are all well above 10 in Table 5 . That is, there is a serious multicollinearity between these variables. Table 6 Correlation test results of factors of PC. lnV lnEEP lnPOP lnPI lnPS lnTP lnV 1 - - - - - lnEEP 0.991** 1 - - - - lnPOP -0.969** -0.976** 1 - - - lnPI 0.981** 0.986** -0.985** 1 - - lnPS 0.962** 0.973** -0.978** 0.985** 1 - lnTP 0.993** 0.995** -0.979** 0.988** 0.971** 1 Table 7 OLS regression results of factors of PC. OLS result Unstandardized coefficient t-Statistic Sig. VIF Constant -53.941 -2.737 - lnV -0.171 -0.395 80.080 lnEEP -8.295 4.454 115.347 lnPOP 1.147 0.646 38.552 lnPI -0.166 -0.441 92.217 lnPS -0.852 -1.236 39.439 lnTP -0.248 -0.707 165.934 Adjusted R 2 0.961 - - F-statistic Sig. 0.000 - - According to the results shown in Table 6 , it is obvious that more than half of the variables are correlated, and the VIF values of all the influencing factors of rural electricity consumption are much higher than 10 in Table 7 . That is, there is a serious multicollinearity between these variables. 5.1.2. Construction of regression equations based on STIRPAT In this paper, ridge regression is used to estimate the coefficients in STIRPAT model for TEC and PC. In the aspect of TEC, according to the ridge regression equation, Fig. 6 and Fig. 7 respectively show the relationship between ridge trace and \({R}^{2}\) and k. Each independent variable first changes rapidly with the increase of k value, and then becomes rapidly stable after k = 0.67. Fig. 6 shows that \({R}^{2}\) of ridge regression changes at a high rate of change before K = 0.67, and the change rate becomes much lower when the value of k is 0.67. Thus, the minimum value of k (k = 0.67) can be performed with a high adjustment \({R}^{2}\) of 0.941. Therefore, it is reasonable to choose k as 0.67 in this paper. F test can be passed and the result of F-Sig is 0.000 0049 < 0.05. This implies that there is a linear relationship between the independent variable TEC and the dependent variables. At the same time, the t-sig of constant term and regression coefficient is less than 0.1, in Table 8 . This indicates that all independent variables should be introduced into the regression equation. Finally, the fitting ridge regression equation of LnTEC is as follows: \({ln}TEC=-3.985+0.226{ln}PAM+1.362{ln}EIA-0.103{ln}EE-\) \(0.500{ln}POP+0.115{ln}V+0.260{ln}PS\) (17) Table 8 Test results of each independent variable and constant for TEC. Variable constant lnPAM lnEIA lnPHI lnPOP lnV lnPS Coefficient’s t Sig -3.985* 0.226* 1.362** -0.103** 0.500*** 0.115*** 0.260** Note: *** significant at 1%, ** significant at 5%,* significant at 10%. In the aspect of PC, ridge regression is used to estimate the coefficients in the STIRPAT model (Fig. 8 and Fig. 9 ), and k = 0.66. F test can be passed and the result of F-Sig is 0.0001732 < 0.05. This implies that there is a linear relationship between the independent variable TEC and the dependent variables. At the same time, the t-sig of constant term and regression coefficient is less than 0.1, in Table 9 . This indicates that all independent variables should be introduced into the regression equation. Finally, the fitting ridge regression equation of LnPC is as follows: $${ln}PE=2.545+0.151{lnV}+0.554{ln}EEP-0.572{ln}POP+0.105{ln}PI+0.213{ln}PS+0.081{ln}TP$$ 18 Table 9 Test results of each independent variable and constant for PC. Variable constant lnV lnEEP lnPOP lnPI lnPS lnTP Coefficient’s t Sig 2.545 * 0.151 *** 0.554 *** -0.572 ** 0.105 *** 0.213 * 0.081 *** Note: *** significant at 1%; ** significant at 5%; * significant at 10%. 5.2. Discussion of regression results 5.2.1. Prediction results of influencing factors In this paper, PSO-BP model is used to predict the factors affecting rural TEC and PC in Henan Province. In addition, the rolling method of the prediction of the first three years and the next year is adopted to obtain the predicted values of influencing factors affecting TEC and PC in Henan Province during 2020-2025, as shown in Table 10 and Table 11 . Table 10 The estimated values of each influence factor of TEC. Year PAM (M▪kw) EIA (M▪ha.) EE POP (M) V (B▪RMB) PS (sq.m.) 2020 12 4.51 5.4367 0.25 49.08 815.4 52.15 2021 12 5.15 5.4369 0.26 47.77 849.7 52.71 2022 12 5.32 5.4383 0.25 46.83 856.5 52.76 2023 12 5.56 5.4376 0.24 46.34 822.2 52.93 2024 12 5.68 5.4380 0.24 46.11 821.1 52.93 2025 12 5.74 5.4379 0.25 46.03 869.4 52.98 From Table 10 , it can be seen that PAM, V, and PS increase to varying degrees over time from 2020 to 2025, POP shows a decreasing trend, also EIA and EE have changed very little. This means that with the gradual increase of V and PS, the use of coal, oil, natural gas and other primary energy will inevitably increase in rural areas of Henan Province. Figure 10 shows the trends of the influencing factors of TEC from 2009 to 2025. Among them, EE and POP show a decreasing trend, while other influencing factors increased year by year. Table 11 The estimated values of each influence factor of PC. Year V (B▪RMB) EEP (M▪kw) POP (M) PI (RMB) PS (sq.m.) TP (W/ 100 households) 2020 815.4 12.473 49.08 16436 52.15 400790 2021 849.7 12.476 47.77 18242 52.71 418510 2022 856.5 12.477 46.83 19665 52.76 428810 2023 822.2 12.478 46.34 21453 52.93 423440 2024 821.1 12.478 46.11 22996 52.93 428140 2025 869.4 12.478 46.03 25332 52.98 431930 From Table 11 , it can be seen that of the six factors affecting rural PC in Henan Province from 2020 to 2025, only the number of POP is constantly decreasing, while V, EEP, PI, PS and TP are increasing to varying degrees in Henan Province over time. This means that the increase of EEP, PS and TP will directly lead to the increase of rural electricity consumption. The increase of V and PI improves the living standard of villagers and indirectly leads to the increase of PC. Figure 11 shows the trends of the influencing factors of PC from 2009 to 2025. Among them, only POP shows a decreasing trend, while other influencing factors increased year by year. 5.2.2. Prediction results of TEC and PC In order to predict rural TEC and PC in Henan Province, the predicted values of influencing factors in 2020-2025 are put into Equation (17) and Equation ( 18 ) respectively. Thus, the predicted TEC and PC in rural areas of Henan Province from 2020 to 2025 can be calculated, as shown in Table 12 and Table 13 . Table 12 Predicted values of TEC in rural areas of Henan Province from 2020 to 2025. Year TEC (B▪tons) Year TEC (B▪tons) 2020 248.448 2023 258.550 2021 253.012 2024 259.237 2022 257.044 2025 260.165 As shown in Table 12 , from 2020 to 2025, the TEC in Henan Province keeps a steady growth, and the predicted values of TEC in 2020 and 2025 are 24.84 million tons and 26.02 million tons, respectively. The TEC in rural areas of Henan Province in 2025 is 4.72% higher than that in 2020. Table 13 Predicted values of PC in rural areas of Henan Province from 2020 to 2025. Year PC (B▪kw▪h) Year PC (B▪kw▪h) 2020 39.078 2023 41.752 2021 40.498 2024 42.240 2022 41.435 2025 43.034 As shown in Table 13 , the total rural PC in Henan Province keeps a steady growth from 2020 to 2025, and the predicted values of PC in 2020 and 2025 are 39.078 billion kwh and 43.034 billion kwh, respectively. In 2025, rural PC in Henan Province will increase by 10.12% compared with 2020. As can be seen from Fig. 12 , both PC and TEC in Henan Province are increasing year by year. However, the growth range of TEC will decrease significantly from 2022, while that of PC will increase significantly, which means that the implementation of Henan Province's rural revitalization strategic plan has achieved significant results in the key task of "promoting rural energy revolution". Nonetheless, after converting the annual PC in rural areas of Henan Province into standard coal, it accounts for less than 30% of TEC. This shows that rural electrification in Henan Province is at a low level and relies more on fossil energy such as coal, oil and natural gas. According to the forecast values of TEC and PC in Henan Province from 2020 to 2025, in the future, rural TEC in Henan Province will still rely more on fossil energy without transformation. This will undermine the goal of carbon peaking by 2030. Consequently, the realization of Henan Province's rural energy transformation needs the substitution of low-carbon energy and the improvement of electrification level. 6. Conclusions And Policy Implications Rural areas account for 12% of global carbon emissions. China, as a major agricultural country, is exploring a low-carbon transformation model of rural energy as a necessary path to achieve carbon neutrality by 2060. This paper takes rural areas of Henan Province as an example to explore the path suitable for the low-carbon transformation of rural energy in Henan Province. The research results of this paper can be summarized into the following aspects. (1) In this paper, the decoupling states between rural PC and TEC and its influencing factors in Henan Province are analyzed by using decoupling principle. The results show that there are different degrees of decoupling between TEC and PC. (2) Since there is multicollinearity among the 6 influencing factors of TEC, and the same problem exists among the 6 influencing factors of PC. Therefore, based on STIRPAT model, this paper uses ridge regression method to fit the linear equations of TEC and PC, and the validity of the fitted equations are verified. Rather than simply summing up the primary energy consumption of rural Henan Province. (3) In addition, this paper optimizes the BP neural network by PSO, and innovatively predicts the values of influencing factors of PC and TEC. The results show that the influencing factors gradually increase year by year. Among them, EIA and EE are basically unchanged, while POP gradually decreases. It is shown that the increase of V can increase PI, thus improving people's living standard, thus improving the work and study efficiency in rural areas, and conversely improving V. Which also means that each influencing factor is complementary to each other. (4) Finally, the consumption of TEC and PC in 2020-2025 is obtained by STIRPAT using ridge regression. According to the forecast results, the consumption of TEC and PC will continue to increase year by year in 2020-2025. However, from 2022, the growth rate of PC will be greater than that of TEC. This is a good beginning of the low-carbon energy transition in Henan Province. Nonetheless, rural TEC in Henan Province will still be more dependent on fossil fuels in 2020-2025. These conclusions can provide the following applicable policy implications for rural energy upgrading of Henan Province and other rural areas in China. First, rural power grid upgrading should be accelerated. (1) Unified planning. The long-term goal of the unified construction of power grid, unified power grid equipment sequence, integrated distribution of power grid services, narrowing the gap between urban and rural power supply services, and promoting the coordinated development of urban and rural power grids. (2) Classified development. First, in light of different regions, strengthen power grids in cities and industrial clusters. Second, according to different voltage levels, the development of strong simplification and orderly, speed up the construction of 110 kv and 10 kv and below power grid, according to the principle of orderly advance and backward optimization of 35 kv power grid. (3) Hierarchical progress by region. While improving the overall power supply capacity of rural power grid in the whole province, we should adhere to the principle of "raising the low and controlling the high", improve the grid load ratio in backward areas, and control the load ratio in areas exceeding the upper limit of the guidelines. (4) High-standard construction. (5) Primary and secondary coordination. Second, promote renewable energy generation. According to the characteristics of rural regional distribution and resource endowment, different power supply should be adopted. Clean and renewable energy should be the focus of future rural development in Henan Province under the "dual carbon" target. To build a clean and low-carbon rural energy supply system, with the focus on improving the intelligence level of power grids, promote the development of small hydropower, photovoltaic, wind power, methane and other renewable energy sources in rural areas, implement the substitution of electric energy and multi-energy complementarity, improve energy efficiency, and eliminate prominent problems such as high pollution and low energy efficiency in rural development. Third, promote electric energy substitution projects. (1) To improve the quality of agriculture and promote the electrification of agricultural production. First, centering on the goal of high-standard farmland in Henan, the government should promote comprehensive matching of farmland, water, forest, roads and electricity, and turn more "wang-tian farmland" into "high-yield farmland". Second, promoting the application of technologies such as electric flue-cured tobacco, electric tea production, electric grain drying, heat pump drying of wood, and cold storage of fruits and vegetables, so as to ensure power consumption of intelligent planting and breeding bases and enhance agricultural electrification. (2) Build beautiful villages and electrify rural residents. First, choose heating technology according to local conditions. Second, do a good job in the construction of supporting power grids in the province's 400 villages with tourism characteristics and "1000 villages demonstration" villages. Third, accelerate the layout of charging infrastructure networks, and promote clean replacement projects for different types of vehicles, including buses, taxis, municipal and official vehicles, and private passenger vehicles. Fourth, in the establishment of national subsidy policies, should be based on the level of agricultural development in the region. The improvement of agricultural mechanization level can promote rural economy and urbanization level. Thus, the V and PI of rural areas are improved, and the living standards of rural areas are improved, so that the work efficiency of rural areas is further improved, and a virtuous cycle is finally formed, which is the fundamental power of rural energy upgrading in Henan Province. The upgrade of rural energy can improve the utilization efficiency of rural energy, promote the great development of rural economy, improve the income of rural areas and offset the high cost of clean energy. Therefore, on the one hand, attention should be paid to the collaborative promotion of TEC and PC, while improving the use of PC in rural areas, the use quality of TEC should be improved, so as to reduce CO2 emissions in rural areas. On the other hand, agricultural mechanization can increase V and PI, thereby boosting the rural economy and thus offsetting the additional costs of renewable energy. Abbreviations 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 Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The authors declare that data supporting the findings of this study are available within the article. Competing interests The authors declare no conflict of interest. Funding This manuscript unfunded support Authors' contributions 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. 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 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 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 Reports 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. Sustainable Cities and Society 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. Sustainable Cities and Society 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. Sustainable Cities and Society 56:102068. https://doi.org/10.1016/j.scs.2020.102068 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. Sustainable Cities and Society 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. Sustainable Cities and Society 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. Renewable 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 Li M, Jin T, Liu S, Zhou S (2021) The cost of clean energy transition in rural China: Evidence based on marginal treatment effects. Energy Econ 97:105167. https://doi.org/10.1016/j.eneco.2021.105167 Lin B, Li Z (2020) Spatial analysis of mainland cities’ carbon emissions of and around Guangdong-Hong Kong-Macao Greater Bay area. Sustainable Cities and Society 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. Renewable 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. Sustainable Cities and Society 42:344–354. https://doi.org/10.1016/j.scs.2018.02.010 Sedai AK, Nepal R, Jamasb T (2021) Flickering lifelines: Electrification and household welfare in India. Energy Econ 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. Sustainable Cities and Society 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 Research & Social Science 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. Sustainable Energy, Grids and Networks 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, PSO (2020b) Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and. Science of The Total Environment 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 Reports 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. Sustainable Cities and Society 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. Sustainable Cities and Society 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. Sustainable Cities and Society 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 Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Jan, 2022 Reviewers invited by journal 24 Jan, 2022 Editor invited by journal 14 Jan, 2022 Editor assigned by journal 03 Jan, 2022 First submitted to journal 24 Dec, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYNACAyBmBjHYGOTY2JsPkKbFmI/nWAIp1rExJM6TyFHAq0a+vffwizcFdnl8x5mfPfhRZpfexpDDwPCjYhtuJ505l2Y5xyC5WPIwm7lhz7nk3DaGswcYe87cxq1FIsfMmMeAOXHDYQYzCd425tw2xr4EZsY23FrkZ4C11AO1sH+T/NtWn87GDDQBnxaGGznGj3kMDgO18JhJ87YdTmBjI6DF4MwZM8Y5BscTZx7mKZOWOXfcsI2HLeEgPr/It/cYf3jzpzqx7/zxbZJvyqrl5ec/PvjgRwUehwHjQoIHRB1AEjqAVSECMH/A0DIKRsEoGAWjABkAAFMcVbZqJ5wtAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9615-2994","institution":"North China Electric Power University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2021-12-25 08:15:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1203734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1203734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17737206,"identity":"348abe2b-cb1c-4868-a27f-3a968a940e98","added_by":"auto","created_at":"2022-01-28 14:23:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93400,"visible":true,"origin":"","legend":"\u003cp\u003eThe technical roadmap.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/b416d240a2b9b48901b1dac4.png"},{"id":17737205,"identity":"17ba30be-a816-478d-ada8-5bb4334070b0","added_by":"auto","created_at":"2022-01-28 14:23:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62920,"visible":true,"origin":"","legend":"\u003cp\u003eDecoupling elasticity indexes between TEC and influencing factors.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/ab69b7efd7f746f9ef36f24c.png"},{"id":17737782,"identity":"bb82e9b1-b4f4-46a2-9407-2eb24e31487e","added_by":"auto","created_at":"2022-01-28 14:29:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53853,"visible":true,"origin":"","legend":"\u003cp\u003eDecoupling elasticity indexes between PC and influencing factors.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/36c5faaca04e1e1ed6790652.png"},{"id":17737670,"identity":"9b7ad3b5-d5c1-4273-bc2f-5dc4fa670e55","added_by":"auto","created_at":"2022-01-28 14:26:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20136,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of typical N-dimensional input neuron model.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/594909f8afa2991842dbf508.png"},{"id":17737891,"identity":"5862d262-3cb4-4d0d-af73-4dbbdfb56908","added_by":"auto","created_at":"2022-01-28 14:35:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48142,"visible":true,"origin":"","legend":"\u003cp\u003e\tThe flow chart of PSO-BP.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/8d918f21aaee9286dba9946b.png"},{"id":17737863,"identity":"fa925b85-679c-4d1b-ada2-1d0afee3788a","added_by":"auto","created_at":"2022-01-28 14:32:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":49509,"visible":true,"origin":"","legend":"\u003cp\u003e\tRidge regression curves.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/f52acfeeaf20b8b204b3d78a.png"},{"id":17737214,"identity":"5c84ae5e-ea4e-48da-b93d-65f61eea7ffe","added_by":"auto","created_at":"2022-01-28 14:23:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":39332,"visible":true,"origin":"","legend":"\u003cp\u003e\tVariation of K at different RSQ.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/7f8225f0843823e6273fdd60.png"},{"id":17737208,"identity":"4c6a3772-c830-4e26-998e-2a1ee703b137","added_by":"auto","created_at":"2022-01-28 14:23:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":58582,"visible":true,"origin":"","legend":"\u003cp\u003e\tRidge regression curves.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/778f749a219756e1801a5190.png"},{"id":17737674,"identity":"db11879c-a272-4c1f-b897-9c43a251a1bf","added_by":"auto","created_at":"2022-01-28 14:26:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":35287,"visible":true,"origin":"","legend":"\u003cp\u003e\tVariation of K at different RSQ.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/1d3b6ad3eab64e4149e1c66e.png"},{"id":17737212,"identity":"f99a068b-3067-4494-abd1-13df08ceeb61","added_by":"auto","created_at":"2022-01-28 14:23:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":129148,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend chart of six influencing factors of TEC from 2009 to 2025.\u003c/p\u003e","description":"","filename":"fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/d6ff4df40231c612228efdfd.png"},{"id":17737676,"identity":"cef00e6c-63db-43d1-aac0-bbaf7a83a323","added_by":"auto","created_at":"2022-01-28 14:26:35","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":136209,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend chart of six influencing factors of TEC from 2009 to 2025.\u003c/p\u003e","description":"","filename":"fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/8745846c09681c2edcf5e1df.png"},{"id":17737784,"identity":"e5a3ef98-a6f2-4495-a326-3c02fa4cab08","added_by":"auto","created_at":"2022-01-28 14:29:35","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":38330,"visible":true,"origin":"","legend":"\u003cp\u003e\tLine chart of PC and TEC changes from 2020-2025.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/680d43a6e29daaa6de8c8862.png"},{"id":17737892,"identity":"06a25cf4-7b00-4d6e-b008-ecc07c39ee8f","added_by":"auto","created_at":"2022-01-28 14:35:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1041200,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1203734/v1/62a7443c-532a-422e-9aa1-7b169a56bded.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eThe forecasting model research of rural energy transformation in Henan Province based on STIRPAT model\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEnergy is an important material foundation for the survival and development of human society. It is vital to the national economy and people\u0026apos;s livelihood and the strategic competitiveness of a country. In today\u0026apos;s world, the massive use of fossil energy brings about a series of problems in the fields of environment, ecology and global climate change. Countries around the world have taken the initiative to solve the dilemma and accelerate energy transformation and development. Yet now, around 2.8 billion people worldwide still rely on traditional biomass for cooking, heating and so on, 95% of which are concentrated in rural areas in Asia and Africa (Ma et al. \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Han et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eChina is a big agricultural country with vast rural area and the rural population. About half of China\u0026apos;s population lives in the countryside. Since the reform and opening up, the traditional rural energy consumption structure has also undergone significant changes (Jin et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nonetheless, China\u0026apos;s combined agricultural and rural greenhouse gas emissions account for about 15 percent of national emissions (Han et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Promoting rural energy revolution and realizing rural energy transformation and upgrading are crucial measures to implement the spirit of the 19th CPC National Congress and General Secretary Xi Jinping\u0026apos;s strategic thought on energy revolution. Therefore, it is an effective way to realize the green development of rural areas to explore the path of low-carbon energy transition in China.\u003c/p\u003e\n\u003cp\u003eHenan is the largest agricultural province in central China, with a population of 96.4 million. Residents of Henan Province are heavily concentrated in the countryside. As an important province with a large population and large grain and agricultural production, Henan is the main battlefield of the rural revitalization strategy (Wu et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn January 2017, the People\u0026apos;s Government of Henan Province issued the 13th Five-Year Energy Development Plan of Henan Province, which put forward eight key tasks, including accelerating the development of non-fossil energy, strengthening rural energy construction and building a smart energy system. The supply-side structural reform in the energy sector has achieved remarkable results. The average annual concentration of PM2.5 and PM10 has dropped by more than 30%, and all environmental indicators have reached the best level in the past five years. The 14th Five-Year Plan points out that it is necessary to build a low-carbon and efficient rural energy support system in Henan Province, continue to promote the energy revolution, and actively develop new and renewable energy.\u003c/p\u003e\n\u003cp\u003eIn addition, the strategic Plan for Rural Revitalization of Henan Province (2018-2022) specifies the key tasks of \u0026quot;promoting the rural energy revolution\u0026quot;. It is mainly reflected in optimizing the rural energy supply structure and constructing a clean, low-carbon, safe and efficient modern rural energy system.\u003c/p\u003e\n\u003cp\u003eTherefore, exploring the driving factors of rural energy consumption in Henan Province and predicting the trend of total energy consumption (TEC) and power consumption (PC) in Henan Province are crucial steps to explore the mode of rural energy transformation and upgrading in Henan Province.\u003c/p\u003e\n\u003cp\u003eFor these reasons, the main tasks of this paper are to analyze the current situation of rural energy consumption in Henan Province, explore the driving factors affecting TEC and PC in rural areas of Henan Province, and forecast TEC and PC to analyze their changing trend. So as to explore the low-carbon energy transformation model of Henan Province. In addition, the technical roadmap for this article is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe contributions and originalities of this paper can be summarized as follows:\u003c/p\u003e\n\u003cp\u003e(1) In this paper, the decoupling states between rural PC and TEC and its influencing factors in Henan Province are analyzed by using decoupling principle creatively.\u003c/p\u003e\n \u003cp\u003e(2) Since there is multicollinearity among the 6 influencing factors of TEC, and the same problem exists among the 6 influencing factors of PC. Therefore, based on STIRPAT model, this paper uses ridge regression method to fit the linear equations of TEC and PC originally, and the validity of the fitted equations are verified. Rather than simply summing up the primary energy consumption of rural Henan Province.\u003c/p\u003e\n \u003cp\u003e(3) In addition, this paper optimizes the BP neural network by PSO, and innovatively predicts the values of influencing factors of PC and TEC.\u003c/p\u003e\n \u003cp\u003e(4) Finally, the consumption of TEC and PC in 2020-2025 are obtained by STIRPAT using ridge regression. Also, the forecast results of TEC and PC are analyzed to find the mode of rural energy transformation in Henan Province.\u003c/p\u003e\n\u003cp\u003eThe rest of the article is structured as follows: Section 2 provides a relevant literature review for rural energy consumption, rural power consumption and the application of STIRPAT model. Variable selection for total energy consumption and power consumption of Henan Province are introduced based on Tapio decoupling model in Section 3. Theoretical model construction and data in Section \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Section \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e analyzes and discusses the results of regression equations. Section \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e provides the conclusions and policy recommendations.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003ch2\u003e2.1. Research on rural energy consumption\u003c/h2\u003e\n\u003cp\u003eIn China\u0026apos;s rural areas, energy consumption is mainly used for villagers\u0026apos; life, such as heating, cooking, lighting, transportation and agricultural production activities, such as irrigation, planting, harvesting and fertilization (Ma et al. \u003cspan class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Due to China\u0026apos;s rapid urbanization process, energy consumption in rural areas is increasing. Moreover, a large amount of CO2 gas is emitted, which poses a threat to the health of villagers (Amagai et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTechnological advances in energy efficiency and replacing conventional energy with renewable energy are generally regarded as effective measures to solve the problem of harmful gas emissions (Wu \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Du et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Su et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).However, the consumption of fossil fuels supports the total consumption of rural energy, and the use of clean energy is less.\u003c/p\u003e\n\u003cp\u003eConsequently, many scholars have explored the transformation mode of rural energy. Han et al.(Han et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) used the space-time model to study the space-time characteristics of energy ladder in agriculture and rural life of developing countries, taking China as an example, by analyzing rural life and production activities. From the perspective of information reliability and standard non-compensation, Peng et al.(Peng et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) constructed a decision support framework for managing the choice of new energy in rural areas. This is of great significance to the realization of the national low-carbon sustainable goal. He et al.(He et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) proposed to reduce the total energy consumption in rural areas by improving the energy efficiency of rural buildings. Jia et al.(Jia et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) analyzed the impact of the energy consumption revolution on the health of the elderly in rural areas and its mechanism. And put forward, should improve the rural medical security level. So as to improve the physical and mental health of the elderly to ensure the successful realization of the rural energy revolution. Yahyaoui et al. (Yahyaoui et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) explored the installation scheme of renewable clean energy power generation devices in remote rural areas. It provides important policy suggestions for the green and sustainable development of rural areas. Ma et al. (Ma et al. \u003cspan class=\"CitationRef\"\u003e2021a\u003c/span\u003e) analyzed the current situation of energy consumption in rural China, obtained the main factors affecting rural energy consumption, identified the key factors to improve energy efficiency, and provided a meaningful reference for rural low-carbon energy transformation. Lemence and Tamayao (Lemence and Tamayao \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified hybrid solar systems as a powerful solution for sustainable energy in rural Philippines, and developed a grid connection solution with the lowest cost for solar photovoltaic. Zou and Luo (Zou and Luo \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) used Tobit model to estimate the determinants of energy consumption of rural households in China, based on the data of 1,472 rural households in a comprehensive survey. The results show that households with higher economic and educational levels have higher levels of electrification of energy consumption (Zou and Luo \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Imran et al. (Imran et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) collected primary data from 196 households in four districts of Pakistan\u0026apos;s Punjab province to explore the impact of biomass energy on human health in rural areas. Zi et al. (Zi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyzed the cooking energy mode in rural areas of Henan Province and learned that the low-carbon transformation of cooking energy is of great help to the transformation of rural energy.\u003c/p\u003e\n\u003cp\u003eThus, only by analyzing the current situation of rural energy consumption can we find a way out of rural energy transformation. However, most of the above literature is cross-regional analysis of the path of national rural energy transformation. Hence, this paper focuses on the analysis of the current situation of rural energy in Henan Province.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.2. Research on rural power consumption\u003c/h2\u003e\n \u003cp\u003eStudies on power consumption in rural areas have found that, with the improvement of rural living standards, energy consumption increases and electricity consumption also shows a rising trend (Tesfamichael et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Agrawal et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The increase of per capita income makes the transformation of rural energy use to commercialization (Asmare et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). That is, the villagers will gradually shift their dependence on non-commercial energy sources such as straw, firewood and bulk coal to electricity (Riva and Colombo \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The improvement of rural electrification can not only improve the efficiency of people\u0026apos;s work and study, but also diversify people\u0026apos;s ways of entertainment and study, thus improving people\u0026apos;s physical and mental health (Han et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Electrification has also increased the use of household appliances in rural areas and improved the quality of life of the people (Mahajan et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), thereby changing lifestyles, improving the per capita income, and accelerating the pace of urbanization in rural areas towards a relatively well-off society (Sedai et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eYet 1.2 billion people still do not use electricity, and most of them live in rural areas (Robert and Gopalan \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vinicius et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the driving factors for improving rural electrification level should be explored while improving grid coverage.\u003c/p\u003e\n \u003cp\u003eMa et al. proposed that the purchase rate of energy-saving household appliances is greatly affected by the education level of rural households, per capita income and government subsidies (Ma et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), as well as the influence of place and custom (Zou and Mishra \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). (Rahman et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) analyzed that the success of rural electrification program in Bangladesh is significantly related to the driving factors of system investment, community participation, anti-corruption characteristics, standardized practices and performance incentives. It provides a meaningful reference for rural electrification in other countries. Minaei et al. (Minaei et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) proposed to improve rural electrification by improving the quality of life in rural and remote areas through clean electricity.\u003c/p\u003e\n \u003cp\u003eNonetheless, these studies only focus on the influencing factors of rural electrification from a single perspective. Correspondingly, the task of this paper is to explore the driving factors affecting rural electricity consumption from various aspects.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.3. Research on STIRPAT\u003c/h2\u003e\n \u003cp\u003eSTIRPAT model has been successfully applied to study the environmental pollution caused by various factors. Based on STIRPAT model, the scenario of peak household carbon dioxide emissions at provincial level in China was simulated by Zhao et al.(Zhao et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Wu et al., Ghazali and Ali (Ghazali and Ali \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wu et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, p. 2) used the extended STIRPAT model to analyze the drivers of the downward trend of CO2 emissions in developed countries. Using the extended STIRPAT model, Lin et al.(Lin et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) explores the impact of urbanization and real economy development on co2 emissions in developing countries.\u003c/p\u003e\n \u003cp\u003eAlso, some literatures also use STIRPAT model to explore the driving factors of energy consumption. Gani (Gani \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) researched the impact of fossil fuel power generation on environmental quality, based on a STIRPAT model that incorporated economic, social and institutional factors. Using STIRPAT model, Shahbaz et al. (Shahbaz et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) re- investigated the relationship between urbanization and energy consumption in Pakistan during 1972Q1-2011Q4. And it confirmed that urbanization increases energy consumption. Employing the extended STIRPAT model, Wang et al. (Wang et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) inspected the effects of population, economic level, technological level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade level on energy consumption in Guangdong from 1980 to 2010. The empirical results show that population, urbanization level, per capita GDP, industrialization level and service level can lead to the increase of total energy consumption, and then increase CO2 emissions.\u003c/p\u003e\n \u003cp\u003eWhereas, there is no literature to assess the main drivers of rural energy in China applying STIRPAT model. Therefore, the primary purpose of this paper is to investigate the driving factors affecting rural energy consumption in Henan Province based on STIRPAT model.\u003c/p\u003e"},{"header":"3. Analysis Of The Current Situation Of Rural Energy In Henan Province","content":"\u003cp\u003eThis section introduces the decoupling principle to analyze the influencing factors of rural energy consumption and electricity consumption in Henan Province.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e3.1. Variable selection\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec6\"\u003e\n \u003ch2\u003e3.1.1. The decoupling theory\u003c/h2\u003e\n \u003cp\u003eThe word \u0026quot;decoupling\u0026quot; originated in physics. Decoupling means that environmental pressure and economic growth are no longer interdependent, and changes in environmental pressure indicators such as carbon emissions are no longer synchronized with changes in economic development, and do not affect each other (Gong et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Tapio decoupling model analyzes the decoupling relationship between variables through the concept of elasticity. Tapio decoupling model is not only not affected by the change of statistical dimension, but also can decompose the causal chain of the decoupling index by using the identity, so as to realize the in-depth analysis and research on the influencing factors behind the decoupling state (Duan et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This paper studies the decoupling relationship between rural TEC and its influencing factors in Henan Province and the decoupling relationship between rural PC and its influencing factors.\u003c/p\u003e\n \u003cp\u003eThe expression is as follows:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$D=\\frac{\\varDelta Q/Q}{\\varDelta Yi/Yi}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, D is decoupling elasticity index; Q is rural TEC or EC; Y is the influencing factor.\u003c/p\u003e\n \u003cp\u003eConsequently, the equation of energy influencing factors based on decoupling theory can be written as:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$D=\\frac{\\varDelta E/E}{\\varDelta Wi/Wi}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Wi\\)\u003c/span\u003e\u003c/span\u003e represents the ownership of various factors affecting rural TEC in Henan Province; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta Wi\\)\u003c/span\u003e\u003c/span\u003e represents its increment; E and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta E\\)\u003c/span\u003e\u003c/span\u003e represent the rural TEC and its increment in Henan Province in the current year.\u003c/p\u003e\n \u003cp\u003eConsequently, the equation of electricity influencing factors based on decoupling theory can be written as:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ3\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$D=\\frac{\\varDelta C/C}{\\varDelta Ni/Ni}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, Ni represents the ownership of various factors affecting rural PC in Henan Province; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta Ni\\)\u003c/span\u003e\u003c/span\u003e represents its increment; C and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta C\\)\u003c/span\u003e\u003c/span\u003e represent the rural PC and its increment in Henan Province in the current year.\u003c/p\u003e\n \u003cp\u003eTapio decoupling indicators take the positive and negative values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta Q\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta Y\\)\u003c/span\u003e\u003c/span\u003e, as well as the elastic values of 0.8 and 1.2 as critical values, respectively, to judge the state and degree of decoupling. Eight decoupling states are defined according to the value of decoupling elasticity (Wenbo and Yan \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClassification of Tapio decoupling states.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDecoupling state\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDecoupling type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;Q\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta Yi\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElastic coefficient D\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNegative decoupling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative decoupling of growth\u003c/p\u003e\n \u003cp\u003e(NDG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u0026gt;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong negative decoupling\u003c/p\u003e\n \u003cp\u003e(SND)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u0026lt;0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeak negative decoupling\u003c/p\u003e\n \u003cp\u003e(WND)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026lt;D\u0026lt;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eConcatenate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrowing concatenate\u003c/p\u003e\n \u003cp\u003e(GC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u0026lt;D\u0026lt;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecession concatenate\u003c/p\u003e\n \u003cp\u003e(RC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u0026lt;D\u0026lt;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDecoupling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeak decoupling\u003c/p\u003e\n \u003cp\u003e(WD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026lt;D\u0026lt;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong decoupling\u003c/p\u003e\n \u003cp\u003e(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u0026lt;0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecession decoupling\u003c/p\u003e\n \u003cp\u003e(RD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u0026gt;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec7\"\u003e\n \u003ch2\u003e3.1.2. The variable selection\u003c/h2\u003e\n \u003cp\u003eIn this paper, EIA, PS, PAM, V, POP and EE are taken as the influencing factors of TEC in Henan Province. Based on the data of TEC and influencing factors in rural areas of Henan Province from 2009 to 2019, the decoupling analysis is conducted based on equation (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The results are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDecoupling states between TEC and influencing factors.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEIA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePOP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAccording to the Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, there is a negative decoupling of growth between EIA and total energy consumption TEC. The association between PAM and TEC is negative decoupling of growth and growing concatenate. Also, the relationship between PAM and TEC is negative decoupling of growth and growing concatenate. The relationship between V and TEC shows weak decoupling. POP and EE show strong negative decoupling relationship with TEC.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, EIA, PS, PAM, and V all show positive decoupling effects on TEC. Among them, the most influential factor is the EIA, which shows a trend of wave rise. In addition, the decoupling index of PS, PAM and V have approximately the same trend. However, POP and EE have a negative decoupling effect on TEC, and the decoupling elasticity index of POP is large.\u003c/p\u003e\n \u003cp\u003eIn terms of PC, V, EEP, POP, PI, PS, and TP are taken as the influencing factors of PC in Henan Province. Based on the data of PC and influencing factors in rural areas of Henan Province from 2009 to 2019, the decoupling analysis is conducted based on equation (\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The results are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDecoupling states between PC and influencing factors.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEEP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePOP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the variables V, PI and TP show weak decoupling with rural electricity consumption PC in Henan Province. In addition, the states of PS and EEP are negative decoupling with PC. However, there is a strong negative decoupling between POP and PC.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the analysis of V, PI, PS, EEP and TP from 2009 to 2019 show a positive decoupling effect on PC. Among them, EEP has the most significant impact on PC, the second is the PS, and V, PI and TP have the same influence on PC.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Theoretical Model Construction And Data","content":"\u003cp\u003eIn this study, to fully investigate the characteristics of rural energy usage of Henan Province in total energy consumption (TEC) and power consumption (PC). Firstly, STIRPAT method is used to fit and forecast the total amount of rural energy consumption and electricity consumption in Henan Province, and the model is verified. The next, PSO- BP is involved to predict the change of the influencing factors of TEC and PC in Henan Province.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e4.1. Theoretical model construction\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec10\"\u003e\n \u003ch2\u003e4.1.1. The STIRPAT theory\u003c/h2\u003e\n \u003cp\u003eThe STIRPAT model originated from IPAT(Impact by Population Affluence and Technology), first proposed by Ehrlich and Holdren (1971) (Yang et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gani \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).The IPAT identity (I = PAT) is often used as a basis for investigating the effects of various factors on environmental pollution. Hence, STIRPAT model has become an important tool for analyzing energy consumption and other types of pollution (Huo et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lin and Li \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eI = PAT (4)\u003c/p\u003e\n \u003cp\u003ewhere, I is the impact, which is usually measured by the emission level of pollutants; P is the size of the population; A is the wealth of a country; T is the technical index.\u003c/p\u003e\n \u003cp\u003eIn order to fully study the factors affecting environmental change, IPAT model is too simple and has its limitations. Therefore, using this model as the basis, Dietz and Rosa proposed the STIRPAT model as follows (Huo et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lin and Li \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ4\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$${I}_{t}=a{P}_{t}^{b}{A}_{t}^{c}{T}_{t}^{d}{\\epsilon }_{t}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, a is the intercept term; P, A, and T are the same as in Equation (4); B, C and D represent the elasticity of environmental impact on P, A and T respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{t}\\)\u003c/span\u003e\u003c/span\u003e is a random disturbance, and the subscript t indicates the year.\u003c/p\u003e\n \u003cp\u003eSTIRPAT model has been widely used to analyze the factors affecting environmental pollution(Li et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).To eliminate possible heteroscedasticity, all variables are in logarithmic form. Thus, the equation can be rewritten as:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Ln{I}_{t}\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ln}a\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(b\\left({ln}{p}_{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\left({ln}{A}_{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(d\\left({ln}{T}_{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{t}\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/p\u003e\n \u003cp\u003ewhere, P represents population size (10E4 persons); A is per capita GDP (yuan); T is a technical indicator, measured by energy efficiency (energy consumption in ISI/its actual output \u0026minus; ENE).\u003c/p\u003e\n \u003cp\u003eTo further investigate the driving forces of TEC in rural areas of Henan Province. Based on the specific situation of Henan Province, total rural machinery power, effective irrigation area, energy intensity, rural population, total agricultural output value and per capita housing area of farmers are incorporated into STIRPAT model for improvement and expansion. The rewritten expression is:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ5\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$L{nTEC}_{t}=Lna+{\\beta }_{1}LnPA{M}_{t}+{\\beta }_{2}LnEI{A}_{t}+{\\beta }_{3}LnE{E}_{t}+{\\beta }_{4}LnP{OP}_{t}+{\\beta }_{5}Ln{V}_{t}+{\\beta }_{6}LnP{S}_{t}+{\\xi }_{t}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({TEC}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the rural energy consumption of Henan Province in t years (10E4▪ tons); PAM represents the total power of agricultural machinery; EIA stands for effective irrigation area; EE stands for energy intensity; POP stands for rural population; V represents the total value of agricultural output; PS represents per capita residential area; t is the year.\u003c/p\u003e\n \u003cp\u003eIn addition, total agricultural output value, electric motor power, rural population, per capita income, per capita housing area of farmers and total power of rural durable goods are incorporated into STIRPAT model for improvement and expansion. The econometric model and equation of rural PC in Henan Province is established:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ6\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$$Ln{PC}_{t}=Lna+{\\beta }_{1}Ln{V}_{t}+{\\beta }_{2}LnEE{P}_{t}+{\\beta }_{3}LnPO{P}_{t}+{\\beta }_{4}LnP{I}_{t}+{\\beta }_{5}LnP{S}_{t}+{\\beta }_{6}LnT{P}_{t}+{\\xi }_{t}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, P represents the rural PC of Henan Province (100 million kw▪h); V represents the total agricultural output value; EEP stands for electric motor power; POP stands for rural population; PI stands for per capita income; PS represents per capita housing area; TP represents the total power of rural durable goods.\u003c/p\u003e\n \u003cp\u003eThe relevant variables in Equations (\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) and (\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) are defined in Table Nomenclature.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec11\"\u003e\n \u003ch2\u003e4.1.2. The BP theory\u003c/h2\u003e\n \u003cp\u003eBP network (Back Propagation) was proposed by a group of scientists headed by Rumelhart and McCelland in 1986 (Wen and Yuan \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Kim et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). BP is a kind of multilayer feed-forward network trained by error backpropagation algorithm, which is one of the most widely used neural network models. The learning process of BP includes the forward propagation of information and the back propagation of error. After repeated training, the network weight and deviation changes are calculated continuously in the direction of relative error function gradient descent, gradually approaching the target. A typical three-layer BP neural network structure is shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (Keshtkarbanaeemoghadam et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the figure, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varSigma\\)\u003c/span\u003e\u003c/span\u003e represents weighted sum; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\int\\)\u003c/span\u003e\u003c/span\u003e is the activation function.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003e4.1.3. The particle swarm optimization (PSO) theory\u003c/h2\u003e\n \u003cp\u003eParticle Swarm Optimization (PSO) was proposed by Dr. Eberhart and Dr. Kennedy in 1995 inspired by artificial life. It simulates the random foraging behavior of a flock of birds in space (Suman et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Malik et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAssuming that none of the birds know exactly where the food is, but they do know roughly how far it is, the simplest and most effective method is to search the area around the bird that is currently closest to the food. Hence PSO treats each bird as a particle with position and velocity. Then, the particle uses the closest location it has found to the food and the closest location the group has found so far to change its direction of flight. Finally, the whole population is directed to the same place, and that place is the area closest to the food.\u003c/p\u003e\n \u003cp\u003eIn the process of optimization, the velocity and position of each particle are updated according to the following formulas:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ7\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e$${V}_{i,d}^{T+1}=w{v}_{i,d}^{T}+{c}_{1}{r}_{1}\\left({P}_{\\dot{l}best}-{x}_{i}^{T},d\\right)+{c}_{2}{r}_{2}\\left({g}_{best,d}-{x}_{i,d}^{T}\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Equation\" id=\"Equ8\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e$${x}_{i,d}^{T+1}={x}_{i,d}^{T}+{v}_{i,d}^{T+1}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, d=1, \u0026hellip;,n; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}^{T}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{i}^{T}\\)\u003c/span\u003e\u003c/span\u003e represent the position and velocity of the ith particle in the T iteration respectively. c1 represents self-cognition coefficient; c2 represents social cognition coefficient; r1 and r2 are random numbers between [0,1]; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{\\dot{l}best}\\)\u003c/span\u003e\u003c/span\u003e represents the individual optimal position found by the ith particle until the T iteration; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({g}_{best}\\)\u003c/span\u003e\u003c/span\u003erepresents the global optimal position of the whole population until iteration T; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i,d}^{T+1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V}_{i,d}^{T+1}\\)\u003c/span\u003e\u003c/span\u003e represent the position and velocity of the ith particle obtained after the T+1 iteration of the particle d, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec13\"\u003e\n \u003ch2\u003e4.1.4. The PSO-BP model\u003c/h2\u003e\n \u003cp\u003eBased on the introduction of BP and PSO above, this paper uses PSO to improve BP to predict the changes of various influencing factors (Wen and Yuan \u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e; He et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The specific steps are as follows:\u003c/p\u003e\n \u003cp\u003eStep 1: The fitness function of PSO is set as shown in formula (11).\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ9\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e$$F\\left(z\\right)=e$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere e is the error function in formula (12):\u003c/p\u003e\n \u003cp\u003ee\u0026thinsp;=\u0026thinsp;\u0026lambda;MAE\u0026thinsp;+\u0026thinsp;\u0026micro;RMSE\u0026thinsp;+\u0026thinsp;\u0026eta;MAPE (12)\u003c/p\u003e\n \u003cp\u003ewhere \u0026lambda; and \u0026micro; are non-negative weights. Also, the expressions of MAE and RMSE are shown below:\u003c/p\u003e\n \u003cp\u003eMAE = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{n}\\sum _{i=1}^{n}\\left|{a}_{i}-{\\widehat{a}}_{i}\\right|\\)\u003c/span\u003e\u003c/span\u003e (13)\u003c/p\u003e\n \u003cp\u003eRMSE = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{n}\\sum _{i=1}^{n}\\frac{\\left|{a}_{i}-{\\widehat{a}}_{i}\\right|}{{a}_{i}}\\)\u003c/span\u003e\u003c/span\u003e (14)\u003c/p\u003e\n \u003cp\u003eMAPE = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{n}\\sum _{\\text{i}=1}^{\\text{n}}({\\text{a}}_{\\text{i}}-{\\widehat{\\text{a}}}_{\\text{i}})\u0026sup2;\\)\u003c/span\u003e\u003c/span\u003e (15)\u003c/p\u003e\n \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\widehat{a}}_{i}\\)\u003c/span\u003e\u003c/span\u003e are the actual value and predicted value respectively, and n is the number of samples.\u003c/p\u003e\n \u003cp\u003eStep 2: Initialize the position and velocity of each particle, initialize parameters c1,c2, maximum velocity Vmax, and minimum velocity Vmin, population size N, and maximum iteration Tmax.\u003c/p\u003e\n \u003cp\u003eStep3: The fitness function of each particle in the population is calculated, and the initial individual optimum and the total optimum are obtained.\u003c/p\u003e\n \u003cp\u003eStep4: Update the velocity and position of each particle according to equations (\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) and (\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Also, update individual and global optimality.\u003c/p\u003e\n \u003cp\u003eStep5: If the iteration number is equal to Tmax, the algorithm ends. And output the optimal value and the optimal particle.\u003c/p\u003e\n \u003cp\u003eStep 7: Otherwise, iter = iter+1, returning step 4.\u003c/p\u003e\n \u003cp\u003eStep 8: The value of the optimal particle is taken as the weights and thresholds of BP.\u003c/p\u003e\n \u003cp\u003eStep 9: Finally gets a trained network of BP.\u003c/p\u003e\n \u003cp\u003eThe flow chart of PSO-BP is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. In the figure, E is the error of backpropagation, and \u0026epsilon; is the number that is infinitely close to 0.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u0026nbsp; \u0026nbsp;(16)\u003c/p\u003e\n \u003cp\u003ewhere, O is the output vector of the output layer; d is the expected output vector; x is the expected input vector; l is the dimension of the output vector of the output layer; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(w\\)\u003c/span\u003e\u003c/span\u003e is the connection weight between the output layer and the hidden layer; v is the connection weight between the input layer and the hidden layer; m is the dimension of the hidden layer output vector.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e4.2. Data\u003c/h2\u003e\n \u003cp\u003eData were mainly drawn from government statistical yearbooks. The energy consumption and power consumption of Henan Province are from China Energy Statistical Yearbook (National Bureau of Statistics, 2020). The data of influencing factors total power of agricultural machinery, effective irrigation area, energy intensity, rural population, per capita housing area, total value of agricultural output, electric motor power, per capita income, and total power of rural durable goods are from the Statistical Yearbook of Henan Province (Henan Bureau of Statistics, 2020).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Results And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003e5.1. Recognition results of regression equation\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec17\"\u003e\n \u003ch2\u003e5.1.1. Multicollinearity analysis\u003c/h2\u003e\n \u003cp\u003eThe multicollinearity test is performed based on the data of each influencing factor and rural TEC and rural PC from 2009 to 2019. The multicollinearity test results of various influencing factors of rural TEC are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The multicollinearity test results of various influencing factors of rural PC are shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation test results of factors of TEC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPAM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnEIA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnEE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnEIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.904**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.973**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.856**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.898**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.927**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.980**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.776**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.944**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOLS regression results of factors of TEC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOLS result\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnstandardized coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnEIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic Sig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAccording to the results shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, it is clear that more than half of the variables are correlated, among which the relationship values of total agricultural machinery power, energy efficiency, population, gross output value and housing area per capita are all high.\u003c/p\u003e\n \u003cp\u003eIn addition, VIF is the most common collinearity evaluation standard. The VIF values of agricultural machinery power, population, total agricultural output value and housing area per capita, energy efficiency are all well above 10 in Table\u0026nbsp;\u003cspan class=\"InternalRef\" id=\"isPasted\"\u003e5\u003c/span\u003e. That is, there is a serious multicollinearity between these variables.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation test results of factors of PC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnEEP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnTP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnEEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.969**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.976**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.985**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.973**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.978**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.993**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.979**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.971**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab7\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOLS regression results of factors of PC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOLS result\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnstandardized coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-53.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnEEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic Sig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAccording to the results shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, it is obvious that more than half of the variables are correlated, and the VIF values of all the influencing factors of rural electricity consumption are much higher than 10 in Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. That is, there is a serious multicollinearity between these variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec18\"\u003e\n \u003ch2\u003e5.1.2. Construction of regression equations based on STIRPAT\u003c/h2\u003e\n \u003cp\u003eIn this paper, ridge regression is used to estimate the coefficients in STIRPAT model for TEC and PC.\u003c/p\u003e\n \u003cp\u003eIn the aspect of TEC, according to the ridge regression equation, Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e respectively show the relationship between ridge trace and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e and k. Each independent variable first changes rapidly with the increase of k value, and then becomes rapidly stable after k = 0.67. Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e of ridge regression changes at a high rate of change before K = 0.67, and the change rate becomes much lower when the value of k is 0.67. Thus, the minimum value of k (k = 0.67) can be performed with a high adjustment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e of 0.941. Therefore, it is reasonable to choose k as 0.67 in this paper.\u003c/p\u003e\n \u003cp\u003eF test can be passed and the result of F-Sig is 0.000 0049 \u0026lt; 0.05. This implies that there is a linear relationship between the independent variable TEC and the dependent variables. At the same time, the t-sig of constant term and regression coefficient is less than 0.1, in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. This indicates that all independent variables should be introduced into the regression equation. Finally, the fitting ridge regression equation of LnTEC is as follows:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ln}TEC=-3.985+0.226{ln}PAM+1.362{ln}EIA-0.103{ln}EE-\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0.500{ln}POP+0.115{ln}V+0.260{ln}PS\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (17)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab8\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest results of each independent variable and constant for TEC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003econstant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPAM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnEIA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPHI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient\u0026rsquo;s t Sig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.985*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.362**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.103**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.500***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.260**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: *** significant at 1%, ** significant at 5%,* significant at 10%.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the aspect of PC, ridge regression is used to estimate the coefficients in the STIRPAT model (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e), and k = 0.66.\u003c/p\u003e\n \u003cp\u003eF test can be passed and the result of F-Sig is 0.0001732 \u0026lt; 0.05. This implies that there is a linear relationship between the independent variable TEC and the dependent variables. At the same time, the t-sig of constant term and regression coefficient is less than 0.1, in Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. This indicates that all independent variables should be introduced into the regression equation. Finally, the fitting ridge regression equation of LnPC is as follows:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ10\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e$${ln}PE=2.545+0.151{lnV}+0.554{ln}EEP-0.572{ln}POP+0.105{ln}PI+0.213{ln}PS+0.081{ln}TP$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e18\u003c/div\u003e\n \u003c/div\u003e\n \u003ctable border=\"1\" id=\"Tab9\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest results of each independent variable and constant for PC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003econstant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnEEP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnPS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnTP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient\u0026rsquo;s t Sig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.545\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.554\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.572\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: *** significant at 1%; ** significant at 5%; * significant at 10%.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec19\"\u003e\n \u003ch2\u003e5.2. Discussion of regression results\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec20\"\u003e\n \u003ch2\u003e5.2.1. Prediction results of influencing factors\u003c/h2\u003e\n \u003cp\u003eIn this paper, PSO-BP model is used to predict the factors affecting rural TEC and PC in Henan Province. In addition, the rolling method of the prediction of the first three years and the next year is adopted to obtain the predicted values of influencing factors affecting TEC and PC in Henan Province during 2020-2025, as shown in Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab10\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe estimated values of each influence factor of TEC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAM\u003c/p\u003e\n \u003cp\u003e(M▪kw)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEIA\u003c/p\u003e\n \u003cp\u003e(M▪ha.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePOP\u003c/p\u003e\n \u003cp\u003e(M)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003cp\u003e(B▪RMB)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003cp\u003e(sq.m.)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e815.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e849.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e856.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e822.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e821.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e869.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, it can be seen that PAM, V, and PS increase to varying degrees over time from 2020 to 2025, POP shows a decreasing trend, also EIA and EE have changed very little. This means that with the gradual increase of V and PS, the use of coal, oil, natural gas and other primary energy will inevitably increase in rural areas of Henan Province.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e shows the trends of the influencing factors of TEC from 2009 to 2025. Among them, EE and POP show a decreasing trend, while other influencing factors increased year by year.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab11\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe estimated values of each influence factor of PC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003cp\u003e(B▪RMB)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEEP\u003c/p\u003e\n \u003cp\u003e(M▪kw)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePOP\u003c/p\u003e\n \u003cp\u003e(M)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003cp\u003e(RMB)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003cp\u003e(sq.m.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003cp\u003e(W/ 100 households)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e815.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e400790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e849.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e418510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e856.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e428810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e822.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e423440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e821.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e428140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e869.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e431930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom Table\u0026nbsp;\u003cspan class=\"InternalRef\" id=\"isPasted\"\u003e11\u003c/span\u003e, it can be seen that of the six factors affecting rural PC in Henan Province from 2020 to 2025, only the number of POP is constantly decreasing, while V, EEP, PI, PS and TP are increasing to varying degrees in Henan Province over time.\u003c/p\u003e\n \u003cp\u003eThis means that the increase of EEP, PS and TP will directly lead to the increase of rural electricity consumption. The increase of V and PI improves the living standard of villagers and indirectly leads to the increase of PC.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e shows the trends of the influencing factors of PC from 2009 to 2025. Among them, only POP shows a decreasing trend, while other influencing factors increased year by year.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec21\"\u003e\n \u003ch2\u003e5.2.2. Prediction results of TEC and PC\u003c/h2\u003e\n \u003cp\u003eIn order to predict rural TEC and PC in Henan Province, the predicted values of influencing factors in 2020-2025 are put into Equation (17) and Equation (\u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003e) respectively.\u003c/p\u003e\n \u003cp\u003eThus, the predicted TEC and PC in rural areas of Henan Province from 2020 to 2025 can be calculated, as shown in Table \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab12\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredicted values of TEC in rural areas of Henan Province from 2020 to 2025.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTEC (B▪tons)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTEC (B▪tons)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e248.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e258.550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e253.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e257.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e, from 2020 to 2025, the TEC in Henan Province keeps a steady growth, and the predicted values of TEC in 2020 and 2025 are 24.84 million tons and 26.02 million tons, respectively. The TEC in rural areas of Henan Province in 2025 is 4.72% higher than that in 2020.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable border=\"1\" id=\"Tab13\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredicted values of PC in rural areas of Henan Province from 2020 to 2025.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC (B▪kw▪h)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC (B▪kw▪h)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e, the total rural PC in Henan Province keeps a steady growth from 2020 to 2025, and the predicted values of PC in 2020 and 2025 are 39.078 billion kwh and 43.034 billion kwh, respectively. In 2025, rural PC in Henan Province will increase by 10.12% compared with 2020.\u003c/p\u003e\n \u003cp\u003eAs can be seen from Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e, both PC and TEC in Henan Province are increasing year by year. However, the growth range of TEC will decrease significantly from 2022, while that of PC will increase significantly, which means that the implementation of Henan Province\u0026apos;s rural revitalization strategic plan has achieved significant results in the key task of \u0026quot;promoting rural energy revolution\u0026quot;.\u003c/p\u003e\n \u003cp\u003eNonetheless, after converting the annual PC in rural areas of Henan Province into standard coal, it accounts for less than 30% of TEC. This shows that rural electrification in Henan Province is at a low level and relies more on fossil energy such as coal, oil and natural gas. According to the forecast values of TEC and PC in Henan Province from 2020 to 2025, in the future, rural TEC in Henan Province will still rely more on fossil energy without transformation. This will undermine the goal of carbon peaking by 2030.\u003c/p\u003e\n \u003cp\u003eConsequently, the realization of Henan Province\u0026apos;s rural energy transformation needs the substitution of low-carbon energy and the improvement of electrification level.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"6. Conclusions And Policy Implications","content":"\u003cp\u003eRural areas account for 12% of global carbon emissions. China, as a major agricultural country, is exploring a low-carbon transformation model of rural energy as a necessary path to achieve carbon neutrality by 2060. This paper takes rural areas of Henan Province as an example to explore the path suitable for the low-carbon transformation of rural energy in Henan Province. The research results of this paper can be summarized into the following aspects.\u003c/p\u003e\n\u003cp\u003e(1) In this paper, the decoupling states between rural PC and TEC and its influencing factors in Henan Province are analyzed by using decoupling principle. The results show that there are different degrees of decoupling between TEC and PC.\u003c/p\u003e\n \u003cp\u003e(2) Since there is multicollinearity among the 6 influencing factors of TEC, and the same problem exists among the 6 influencing factors of PC. Therefore, based on STIRPAT model, this paper uses ridge regression method to fit the linear equations of TEC and PC, and the validity of the fitted equations are verified. Rather than simply summing up the primary energy consumption of rural Henan Province.\u003c/p\u003e\n \u003cp\u003e(3) In addition, this paper optimizes the BP neural network by PSO, and innovatively predicts the values of influencing factors of PC and TEC. The results show that the influencing factors gradually increase year by year. Among them, EIA and EE are basically unchanged, while POP gradually decreases. It is shown that the increase of V can increase PI, thus improving people\u0026apos;s living standard, thus improving the work and study efficiency in rural areas, and conversely improving V. Which also means that each influencing factor is complementary to each other.\u003c/p\u003e\n \u003cp\u003e(4) Finally, the consumption of TEC and PC in 2020-2025 is obtained by STIRPAT using ridge regression. According to the forecast results, the consumption of TEC and PC will continue to increase year by year in 2020-2025. However, from 2022, the growth rate of PC will be greater than that of TEC. This is a good beginning of the low-carbon energy transition in Henan Province. Nonetheless, rural TEC in Henan Province will still be more dependent on fossil fuels in 2020-2025.\u003c/p\u003e\n\u003cp\u003eThese conclusions can provide the following applicable policy implications for rural energy upgrading of Henan Province and other rural areas in China.\u003c/p\u003e\n\u003cp\u003eFirst, rural power grid upgrading should be accelerated. (1) Unified planning. The long-term goal of the unified construction of power grid, unified power grid equipment sequence, integrated distribution of power grid services, narrowing the gap between urban and rural power supply services, and promoting the coordinated development of urban and rural power grids. (2) Classified development. First, in light of different regions, strengthen power grids in cities and industrial clusters. Second, according to different voltage levels, the development of strong simplification and orderly, speed up the construction of 110 kv and 10 kv and below power grid, according to the principle of orderly advance and backward optimization of 35 kv power grid. (3) Hierarchical progress by region. While improving the overall power supply capacity of rural power grid in the whole province, we should adhere to the principle of \u0026quot;raising the low and controlling the high\u0026quot;, improve the grid load ratio in backward areas, and control the load ratio in areas exceeding the upper limit of the guidelines. (4) High-standard construction. (5) Primary and secondary coordination.\u003c/p\u003e\n\u003cp\u003eSecond, promote renewable energy generation. According to the characteristics of rural regional distribution and resource endowment, different power supply should be adopted. Clean and renewable energy should be the focus of future rural development in Henan Province under the \u0026quot;dual carbon\u0026quot; target. To build a clean and low-carbon rural energy supply system, with the focus on improving the intelligence level of power grids, promote the development of small hydropower, photovoltaic, wind power, methane and other renewable energy sources in rural areas, implement the substitution of electric energy and multi-energy complementarity, improve energy efficiency, and eliminate prominent problems such as high pollution and low energy efficiency in rural development.\u003c/p\u003e\n\u003cp\u003eThird, promote electric energy substitution projects. (1) To improve the quality of agriculture and promote the electrification of agricultural production. First, centering on the goal of high-standard farmland in Henan, the government should promote comprehensive matching of farmland, water, forest, roads and electricity, and turn more \u0026quot;wang-tian farmland\u0026quot; into \u0026quot;high-yield farmland\u0026quot;. Second, promoting the application of technologies such as electric flue-cured tobacco, electric tea production, electric grain drying, heat pump drying of wood, and cold storage of fruits and vegetables, so as to ensure power consumption of intelligent planting and breeding bases and enhance agricultural electrification. (2) Build beautiful villages and electrify rural residents. First, choose heating technology according to local conditions. Second, do a good job in the construction of supporting power grids in the province\u0026apos;s 400 villages with tourism characteristics and \u0026quot;1000 villages demonstration\u0026quot; villages. Third, accelerate the layout of charging infrastructure networks, and promote clean replacement projects for different types of vehicles, including buses, taxis, municipal and official vehicles, and private passenger vehicles.\u003c/p\u003e\n\u003cp\u003eFourth, in the establishment of national subsidy policies, should be based on the level of agricultural development in the region. The improvement of agricultural mechanization level can promote rural economy and urbanization level. Thus, the V and PI of rural areas are improved, and the living standards of rural areas are improved, so that the work efficiency of rural areas is further improved, and a virtuous cycle is finally formed, which is the fundamental power of rural energy upgrading in Henan Province. The upgrade of rural energy can improve the utilization efficiency of rural energy, promote the great development of rural economy, improve the income of rural areas and offset the high cost of clean energy. Therefore, on the one hand, attention should be paid to the collaborative promotion of TEC and PC, while improving the use of PC in rural areas, the use quality of TEC should be improved, so as to reduce CO2 emissions in rural areas. On the other hand, agricultural mechanization can increase V and PI, thereby boosting the rural economy and thus offsetting the additional costs of renewable energy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003eNomenclature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003eTEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eTotal rural energy consumption in Henan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003e10E\u0026nbsp;4▪tons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003ePAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eTotal power of agricultural machinery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003e10E4▪kw\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003eEIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eEffective irrigation area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003e10E3▪ha.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eEnergy intensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003ePOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eRural population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003e10E4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eTotal value of agricultural output\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003e10E8▪RMB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003ePer capita housing area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003esq.m.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eRural power consumption in Henan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003e10E8\u0026nbsp;▪kw\u0026nbsp;▪h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003eEEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eElectric motor power\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003e10E4▪kw\u0026nbsp;▪h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003ePer capita income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003eRMB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.08465608465608%\"\u003e\n \u003cp\u003eTotal power of rural durable goods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.631393298059965%\"\u003e\n \u003cp\u003eW/ 100 households\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that data supporting the findings of this study are available within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript unfunded support\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgrawal S, Harish SP, Mahajan A et al (2020) Influence of improved supply on household electricity consumption - Evidence from rural India. Energy 211:118544. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.energy.2020.118544\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmagai 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\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.iatssr.2013.11.001\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsmare F, Jaraitė J, Kažukauskas A (2021) The effect of descriptive information provision on electricity consumption: Experimental evidence from Lithuania. Energy Econ 104:105687. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eneco.2021.105687\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enpol.2021.112235\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.149528\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGani A (2021) Fossil fuel energy and environmental performance in an extended STIRPAT model. J Clean Prod 297:126526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2021.126526\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhazali 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 Reports 5:242\u0026ndash;252. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.egyr.2019.02.006\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong Q, Guo G, Li S, Liang X (2021) Decoupling of urban economic growth and water consumption in Chongqing and Chengdu from the \u0026ldquo;production-living-ecological\u0026rdquo; perspective. Sustainable Cities and Society 75:103395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2021.103395\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan J, Zhang L, Li Y (2021) Spatiotemporal analysis of rural energy transition and upgrading in developing countries: The case of China. Appl Energy 118225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apenergy.2021.118225\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan 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\u0026ndash;483. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.econmod.2019.11.025\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe B-J, Yang L, Ye M (2014) Building energy efficiency in China rural areas: Situation, drawbacks, challenges, corresponding measures and policies. Sustainable Cities and Society 11:7\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2013.11.005\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe 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\u0026ndash;21070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-020-08630-6\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuo 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. Sustainable Cities and Society 56:102068. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2020.102068\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImran M, Ozcatalbas O, Bakhsh K (2019) Rural household preferences for cleaner energy sources in Pakistan. Environ Sci Pollut Res 26:22783\u0026ndash;22793. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-019-05588-y\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.150755\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin 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\u0026ndash;21320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-019-05439-w\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeshtkarbanaeemoghadam A, Dehghanbanadaki A, Kaboli MH (2018) Estimation and optimization of heating energy demand of a mountain shelter by soft computing techniques. Sustainable Cities and Society 41:728\u0026ndash;748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2018.06.008\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim 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. Sustainable Cities and Society 62:102321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2020.102321\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemence ALG, Tamayao M-AM (2021) Energy consumption profile estimation and benefits of hybrid solar energy system adoption for rural health units in the Philippines. Renewable Energy 178:651\u0026ndash;668. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.renene.2021.06.090\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Mu H, Zhang M, Li N (2011) Analysis on influence factors of China\u0026rsquo;s CO2 emissions based on Path\u0026ndash;STIRPAT model. Energy Policy 39:6906\u0026ndash;6911. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enpol.2011.08.056\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Jin T, Liu S, Zhou S (2021) The cost of clean energy transition in rural China: Evidence based on marginal treatment effects. Energy Econ 97:105167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eneco.2021.105167\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin B, Li Z (2020) Spatial analysis of mainland cities\u0026rsquo; carbon emissions of and around Guangdong-Hong Kong-Macao Greater Bay area. Sustainable Cities and Society 61:102299. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2020.102299\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin 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\u0026ndash;966. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2017.08.107\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa B, Yu Y, Urban F (2018) Green transition of energy systems in rural China: National survey evidence of households\u0026rsquo; discrete choices on water heaters. Energy Policy 113:559\u0026ndash;570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enpol.2017.11.046\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enpol.2021.112638\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa 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\u0026ndash;36741. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-021-13213-0\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahajan 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\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.esd.2020.04.005\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik 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\u0026ndash;39158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-021-13445-0\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinaei 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. Renewable Energy 176:334\u0026ndash;345. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.renene.2021.05.087\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.energy.2021.121435\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman 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\u0026ndash;851. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enpol.2013.06.100\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiva 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\u0026ndash;143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.esd.2020.04.001\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobert FC, Gopalan S (2018) Low cost, highly reliable rural electrification through a combination of grid extension and local renewable energy generation. Sustainable Cities and Society 42:344\u0026ndash;354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2018.02.010\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSedai AK, Nepal R, Jamasb T (2021) Flickering lifelines: Electrification and household welfare in India. Energy Econ 94:104975. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eneco.2020.104975\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahbaz M, Chaudhary AR, Ozturk I (2017) Does urbanization cause increasing energy demand in Pakistan? Empirical evidence from STIRPAT model. Energy 122:83\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.energy.2017.01.080\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enpol.2021.112732\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuman GK, Guerrero JM, Roy OP (2021) Optimisation of solar/wind/bio-generator/diesel/battery based microgrids for rural areas: A PSO-GWO approach. Sustainable Cities and Society 67:102723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2021.102723\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesfamichael 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 Research \u0026amp; Social Science 63:101394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.erss.2019.101394\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVinicius GTF, Silvia C, Aleksandar D et al (2021) Rural electrification planning based on graph theory and geospatial data: A realistic topology oriented approach. Sustainable Energy, Grids and Networks 28:100525. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.segan.2021.100525\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang 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\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apenergy.2013.01.036\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen 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\u0026ndash;22032. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-020-08418-8\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen L, Yuan X, PSO (2020b) Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and. Science of The Total Environment 718:137194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2020.137194\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenbo G, Yan C (2018) Assessing the efficiency of China\u0026rsquo;s environmental regulation on carbon emissions based on Tapio decoupling models and GMM models. Energy Reports 4:713\u0026ndash;723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.egyr.2018.10.007\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu 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\u0026ndash;846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2015.07.101\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q (2021) Optimization of AI-driven communication systems for green hospitals in sustainable cities. Sustainable Cities and Society 72:103050. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2021.103050\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rser.2021.111328\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu S (2020) The evolution of rural energy policies in China: A review. Renew Sustain Energy Rev 119:109584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rser.2019.109584\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYahyaoui I, Yahyaoui A, Chaabene M, Tadeo F (2016) Energy management for a stand-alone photovoltaic-wind system suitable for rural electrification. Sustainable Cities and Society 25:90\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2015.12.002\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang S, Cao D, Lo K (2018) Analyzing and optimizing the impact of economic restructuring on Shanghai\u0026rsquo;s carbon emissions using STIRPAT and NSGA-II. Sustainable Cities and Society 40:44\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2018.03.030\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.151098\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZi 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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rser.2021.111142\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou B, Luo B (2019) Rural household energy consumption characteristics and determinants in China. Energy 182:814\u0026ndash;823. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.energy.2019.06.048\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou B, Mishra AK (2020) Appliance usage and choice of energy-efficient appliances: Evidence from rural Chinese households. Energy Policy 146:111800. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enpol.2020.111800\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rural power consumption, Rural power consumption, Rural energy transformation, Henan Province, STIRPAT model","lastPublishedDoi":"10.21203/rs.3.rs-1203734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1203734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to find the model of rural energy transformation in Henan Province. This paper examines the effects of population, per capita income, total value of agricultural output, per capita housing area, electric motor power, total power of rural durable goods on rural power consumption (PC) and effective irrigation area, per capita housing area, total power of agricultural machinery, total value of agricultural output, rural population, and energy intensity on rural total energy consumption (TEC) employing Tapio decoupling model. In addition, PSO-BOP 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 2020-2025 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.\u003c/p\u003e","manuscriptTitle":"The forecasting model research of rural energy transformation in Henan Province based on STIRPAT model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-28 14:23:33","doi":"10.21203/rs.3.rs-1203734/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2022-01-24T09:54:48+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-24T07:57:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2022-01-14T21:32:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-03T05:46:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2021-12-25T03:15:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9eb62a0e-bbe2-4f78-ab02-9b1860a0626e","owner":[],"postedDate":"January 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-05-23T07:20:44+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-28 14:23:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1203734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1203734","identity":"rs-1203734","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}