Energy Transition Consumption, Climate Risk Regulation and Economic Well-Being of Rural Households

Abstract

Under the background of rural revitalization, energy transition consumption plays a significant role in improving the economic well-being of rural households. Using panel data from the China Family Panel Studies (CFPS), this paper empirically examines the impact of energy transition consumption on rural households’ economic well-being and explores the moderating effects of climate physical risk and climate transition risk. The results show that energy transition consumption significantly enhances the economic well-being of rural households, highlighting its importance in promoting common prosperity in rural areas. However, both climate physical risk and climate transition risk weaken this positive effect, revealing the complex influence of climate risks on rural well-being. Further heterogeneity analysis indicates that the positive impact is more pronounced in first- and second-tier cities and in the eastern regions. The findings offer policy insights for advancing targeted rural energy transition strategies, improving rural resilience to climate risks, and supporting rural revitalization.

1. Introduction

The transformation of the rural energy structure plays a crucial role in advancing China’s rural revitalization and promoting common prosperity among farmers. The Comprehensive Rural Revitalization Plan (2024–2027), issued by the Central Committee of the Communist Party of China and the State Council, emphasizes the need to optimize energy supply, strengthen and upgrade rural power grids, and promote the development of clean energy. The No. 1 Central Document of 2024 highlights the importance of developing distributed renewable energy in rural areas, while the 2025 version further underscores the need to enhance rural electricity security, accelerate the development and utilization of distributed renewable energy, and support the construction of public charging and battery-swapping infrastructure. These initiatives reflect the emerging characteristics of a new quality of productivity in agricultural energy and signal a transition in rural energy systems toward distributed deployment, cleaner usage, and smarter management [1]. However, many rural regions in China, constrained by geographic remoteness and limited economic capacity, face significant gaps in energy infrastructure investment. Substantial disparities remain between rural and urban households in both energy consumption levels and energy structure [2]. Despite policy efforts in recent years to promote electrification and modernize rural energy use, solid fuels such as wood, dung, and coal remain the primary sources of energy for cooking and heating in many rural households [3]. Against the backdrop of the rural revitalization strategy, a thorough examination of the impact mechanisms linking energy transition consumption to the economic well-being of rural households is essential for informing effective rural energy policies and advancing sustainable rural development. Building on this premise, this study draws on data from the China Family Panel Studies (CFPS) to investigate the specific effects of energy transition consumption on rural household economic well-being, as well as the underlying mechanisms at play.

This paper makes two primary contributions. First, it centers on the well-being effects of energy transition consumption, thereby broadening the analytical perspective within rural economic research. While existing literature has predominantly focused on macro-level issues—such as China’s overall energy development patterns, changes in urban–rural energy consumption structures, and the promotion of renewable energy—this study adopts a micro-level perspective. Specifically, it explores how energy transition consumption influences farmers’ income, reduces energy-related expenditures, and optimizes household energy structures. These findings offer empirical support for enhancing rural energy governance in line with the rural revitalization strategy. Second, this study innovatively incorporates climate risk into the analytical framework to examine its moderating effect on the relationship between energy transition consumption and rural household economic well-being. The uncertainty associated with climate change presents multiple challenges for rural energy systems. In particular, extreme weather events not only directly impact agricultural productivity and energy infrastructure, but also undermine the efficiency and reliability of new energy technologies. Furthermore, in the context of increasingly stringent global climate policies, energy restructuring driven by low-carbon transition goals may significantly affect rural households’ access to and affordability of energy. However, existing research has largely overlooked the role of climate risk in shaping this relationship. This study addresses this gap by identifying and quantifying the moderating effects of both physical climate risks and transition climate risks on household energy consumption. By examining how rural households in different regions respond to energy transitions under climate-related constraints, this paper offers a forward-looking policy perspective for rural energy governance. It also contributes new insights into optimizing rural energy policies and enhancing climate resilience within the broader context of low-carbon development.

2. Literature Review

The relationship between energy consumption and economic well-being has attracted considerable scholarly attention. Research on energy transition and consumption spans multiple disciplines, including economics, sociology, and environmental studies. The existing literature primarily focuses on three key areas. First, the measurement of energy transition consumption has evolved over time. Early studies typically relied on single indicators, such as the share of renewable energy consumption [4] or clean energy generation [5], offering a simple and intuitive approach. More recently, scholars both in China and abroad have developed more comprehensive energy assessment frameworks, particularly in the context of measuring energy poverty, and have systematically examined the determinants influencing energy transition [6]. Second, the causes and drivers of the energy transition have been widely explored. Studies have shown that factors such as energy prices, household income, and education levels directly influence the extent of energy transition consumption. Lower energy prices can significantly improve household living standards and enhance subjective well-being, particularly among vulnerable and low-income groups [7]. Moreover, macro-level factors—including infrastructure development and the dynamic adjustment of policy instruments—have jointly shaped China’s energy resource allocation system, contributing to the uneven spatial patterns of energy transition and consumption [8,9]. Third, scholars have examined household responses to the energy transition and the associated governance challenges. In the context of global energy transformation and climate change, extreme weather events induced by climate change can exacerbate the vulnerability of rural energy consumption. Seasonal variations in temperature shocks have markedly different impacts on household energy transition behavior, influencing not only energy expenditures but also per capita income, work–leisure time allocation, and the pace of innovation in renewable energy technologies [10]. These dynamics not only intensify the urgency of effective energy transition governance but also exert long-term effects on household economic well-being.

Economic well-being, as a key indicator of socioeconomic development and living standards, has undergone several stages of scholarly exploration. The first stage focused on the construction of assessment frameworks. Early studies expanded traditional income-consumption models by incorporating indicators such as happiness, life satisfaction, and interpersonal relationships [11]. The second stage emphasized the interdisciplinary mechanisms underlying economic well-being. Economics was the first discipline to incorporate well-being into its research agenda, identifying social mobility and industrial upgrading as essential pathways to enhance individual well-being and promote social progress [12,13]. As research has deepened, the field has increasingly intersected with psychology, political science, and other disciplines, exploring mechanisms such as psychological adaptation and policy impact [14,15], thereby offering complementary and innovative insights into the study of economic well-being.

In the context of energy transition and its impact on economic well-being, scholars have examined how household energy consumption patterns and energy efficiency influence residents’ overall welfare. The reliance on solid fuels—such as coal and firewood—as primary sources of domestic energy not only crowds out essential living expenditures but also increases the financial burden of healthcare. Rural households with more stable energy access and higher socioeconomic status are more likely to adopt sustainable energy sources to improve their quality of life. In the long term, facilitating the transition to cleaner household energy consumption can significantly enhance environmental and low-carbon benefits while reducing the incidence of chronic obstructive pulmonary disease, cardiovascular disease, and stroke among family members [16]. Consequently, this transition contributes to improved economic outcomes for households, strengthens confidence in future living conditions, enhances the residential environment, and promotes both physical and mental well-being.

In summary, existing research has primarily focused on addressing the challenges of rural revitalization and common prosperity, particularly in relation to poverty alleviation and the risk of returning to poverty in remote areas. These studies have highlighted the relationship between energy transition consumption and economic well-being, emphasizing the critical role of energy transition in mitigating energy poverty and enhancing residents’ welfare. However, limited attention has been given to the influence of external environmental factors on energy transition and economic well-being. Among these factors, climate risks can increase uncertainty in agricultural production, elevate operational risks for farmers, disrupt income stability, alter energy supply and demand dynamics, and raise living costs. These effects may, in turn, worsen energy consumption challenges and further erode the economic well-being of rural households. Therefore, this paper focuses on external environmental factors, with particular attention to climate risk, to examine its impact on rural household energy consumption.

3. Theoretical Framework and Hypotheses

Energy transition consumption refers to the fundamental transformation in energy consumption patterns, efficiency, and philosophy that occurs during the shift from traditional to renewable energy sources and from high-carbon to low-carbon systems [17]. Delays in energy transition consumption not only increase the economic burden on farmers and constrain the adoption of innovative agricultural technologies, but also reinforce deprivation in other dimensions—such as education, gender equality, and the accumulation of social capital. Prolonged exposure to energy-related hardships may foster pessimism among vulnerable groups, diminishing their motivation to pursue improved living conditions. This, in turn, leads to lower levels of subjective well-being and ultimately undermines their long-term economic well-being [18]. Conversely, energy transition consumption—facilitated by supportive policies, improved electricity infrastructure, and expanded energy access—enhances air quality and energy availability [19]. These improvements help lift rural households out of energy poverty, play a crucial role in advancing their economic development and quality of life, and serve as a key pathway toward achieving rural prosperity and enhanced economic well-being.

3.1. Direct Effects of Energy Transition Consumption on the Economic Well-Being of Rural Households

According to the scientific theory of rural sustainability, rural areas are conceptualized as complex socio-ecological systems whose sustainability is shaped by various factors such as resource accessibility, infrastructure development, and the interconnectedness between agricultural sustainability, rural community resilience, and residents’ well-being [20]. Building on this framework, energy transition is not merely about access to energy; it also exerts a profound influence on the economic resilience and well-being of rural households. Specifically, energy transition consumption can enhance the economic well-being of rural households in the following ways. First, by improving energy supply, energy stabilization boosts incomes and reduces energy inequality [21]. The development of energy infrastructure enables farmers to access reliable and stable energy sources, significantly improving both the quantity and efficiency of energy use. This, in turn, reduces the production and operational risks associated with energy shortages [22]. The widespread adoption of clean energy facilitates agricultural mechanization, reduces dependence on labor-intensive practices, and promotes income growth, thereby contributing to improved economic well-being. Second, optimizing the rural energy structure and reducing regional spatial differences in ecological and environmental quality [23]. The adequate and rational use of clean energy is essential for the stable functioning of rural socio-ecological systems. Currently, rural areas face dual pressures of resource depletion and carbon emissions. The inefficient use of agricultural resources and low levels of recycling have led to significant resource waste and environmental degradation [24]. Therefore, promoting the adoption of clean energy sources—such as photovoltaic and biomass energy—not only enhances the efficiency of agricultural resource utilization but also improves the rural ecological environment, reduces the living burden of farmers, and fosters a more stable and sustainable environment for economic development.

Based on the above, we propose Hypothesis 1: Energy transition consumption significantly improves the economic well-being of rural households.

3.2. Climate Physical Risk Effects on the Relationship Between Energy Transition Consumption and Economic Well-Being

Climate change-induced uncertainty is among the most pressing global challenges of our time. Climate physical risk refers to the potential loss of property or disruption of livelihoods caused by extreme weather events, which may also affect insurance liabilities and the value of financial assets [25]. These risks may undermine the positive impact of energy transition consumption on economic well-being through several channels. First, climate physical risks can damage energy infrastructure and increase the cost of energy access, thereby reducing energy availability for rural households. According to poverty vulnerability theory, rural agriculture typically occupies a subordinate, passive role within economic systems and often lacks the capacity to cope with risks and recover from climate-induced shocks [26]. Rural households are particularly vulnerable to extreme climate events, facing threats such as infrastructure damage and agricultural disruption. These shocks may alter household energy consumption behavior, aggravate energy insecurity, and place households in increasingly precarious livelihood conditions. Secondly, climate physical risks may adversely affect the economic well-being of farmers by disrupting agricultural production, reducing expected incomes, and increasing the risks and pressures on smallholder livelihoods [27]. According to expected income theory, individuals’ consumption and investment decisions are closely linked to their expectations of future income, which are shaped by factors such as agricultural productivity and the stability of energy supply. Uncertainty related to climate physical risks may lower farmers’ income expectations, making them more inclined to prioritize short-term cash flow and adopt more conservative economic behaviors [28]. Given the high exposure, fragility, and social vulnerability of rural households, many are compelled to allocate significant time and resources to post-disaster recovery efforts—such as repairing damaged homes and restoring farmland—which directly displaces time that could otherwise be used for productive activities [29]. In contexts with limited labor supply, rural households may also be forced to reduce their participation in sideline or off-farm economic activities, leading to a homogenization of income sources. This not only diminishes their capacity for long-term productive investment but also weakens the effectiveness of energy transition efforts, exacerbates economic vulnerability, and ultimately reduces overall household economic well-being.

Based on the above, we propose Hypothesis 2: Climate physical risks weaken the positive impact of energy transition consumption on the economic well-being of rural households.

3.3. Climate Transition Risk as a Moderator in the Relationship Between Energy Transition Consumption and Economic Well-Being

Energy transition consumption is closely aligned with China’s national “dual-carbon” strategy. In this context, low-carbon development has become a central focus of national energy policy. However, the emergence of climate transition risks presents new challenges to the sustainability and stability of the energy transition process. Climate transition risk refers to the economic and financial uncertainties associated with the societal shift toward a low-carbon economy. These risks mainly arise from policy shifts, disruptive technological or business model innovations, and changes in consumer perceptions and sentiments [30]. The dynamic nature of policy implementation and the uneven pace of technological advancement during the climate transition may weaken the impact of energy transition consumption on the economic well-being of rural households. First, policy shifts can trigger conflicts among stakeholders and lead to strategic behavior, undermining policy effectiveness. Policies aimed at promoting energy transition and achieving environmental goals are often characterized by complexity and multi-objective trade-offs [31]. Some local governments, in response to temporal inconsistencies between short-term economic pressures and long-term policy goals, may adopt strategic behaviors, such as prematurely restricting the use of high-carbon energy sources without providing adequate alternatives [32]. In the absence of well-sequenced and inclusive policy transitions, these disruptions may negatively affect rural residents’ economic well-being. Second, the uneven distribution of technological innovation may place rural areas at a disadvantage in the energy transition process. Research, development, and initial deployment of low-carbon technologies are often concentrated in urban areas. This urban–rural divide reinforces existing disparities and reflects ongoing issues of unbalanced and insufficient development in certain regions [2]. Furthermore, the pace of rural energy infrastructure upgrades significantly lags behind that of urban areas, making it difficult for rural residents to fully benefit from the dividends of low-carbon technologies in a timely manner. This time lag in technological diffusion often results in challenges for rural households in accessing a stable and affordable supply of clean energy during the transition period [33]. Moreover, the absence of a robust technical support system in rural regions means that the maintenance and operational costs of new energy equipment remain high, thereby increasing the overall economic burden associated with energy use.

Based on the above discussion, we propose Hypothesis 3: Climate transition risks weaken the positive impact of energy transition consumption on the economic well-being of rural households.

4. Research Design

4.1. Variables and Measurements

4.1.1. Core Explanatory Variable: Energy Transition Consumption

Rural people’s access to reliable, affordable, and sustainable energy services is critical to their quality of life and overall well-being. Rural residents’ access to reliable, affordable, and sustainable energy services is critical to their quality of life and overall well-being. In this paper, rural dwellers are defined as those living in rural areas, based on the National Statistical Office’s urban–rural division. In this study, we constructed multidimensional indicators of energy transition consumption from three key perspectives: affordability, cleanliness, and accessibility of energy services by rural households [34,35]. The affordability dimension reflects the ability of rural households to pay for modern energy services. It is measured based on household energy expenditures and household income levels and reflects the financial burden of energy use relative to income. The cleanliness dimension reflects the degree of optimization and upgrading of the energy consumption structure of rural households. This dimension reflects the demand and preference of rural households for cleaner energy consumption. The accessibility dimension reflects the likelihood of access to energy by rural households. In order to construct the comprehensive index of energy transition consumption, we adopted the entropy weight method [36]. For robustness checks, we additionally employ the equal-weighting method to recalculate the index, thereby validating the consistency of our findings.

4.1.2. Explained Variable: Economic Well-Being of Rural Households

Economic well-being refers to the economic dimension of quality of life and happiness at the individual, household, or national level. It encompasses not only income and wealth accumulation, but also economic security, social equity, and related factors, and serves as an important indicator of the quality of socioeconomic development [37,38]. This study constructed a comprehensive index system for economic well-being from three dimensions: economic status [39], subjective well-being [40], and social capital of rural households [41,42]. The entropy weighting method was utilized to derive the values of these variables to form a comprehensive index reflecting the level of ecological and economic well-being of each farm household [43]. To ensure the robustness of the empirical results, we also adopt the entropy weight–Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to re-estimate the economic well-being index prior to regression analysis during robustness checks.

4.1.3. Moderating Variables: Physical Climate Risk and Climate Transition Risk

As global climate change increasingly affects economic systems, China continues to promote ecological civilization and improve the well-being of its citizens. In this context, we include physical climate risk and climate transition risk as moderating variables. For physical climate risk, we use extreme weather events—specifically, extreme temperatures and precipitation—as proxy indicators. Based on daily meteorological data, we calculate the total number of days that meet the following criteria: temperatures below −10 °C, above 38 °C, or daily precipitation exceeding 50 mm. These values are then used to construct a physical climate risk index. For climate transition risk, we focus primarily on risks arising from policy-related factors associated with the transition to a low-carbon economy [44]. We use the implementation status of the Air Pollution Prevention and Control Action Plan’s key atmospheric control zones as a proxy variable for climate transition risk. Based on the official bulletins of the State Council, regions designated as pilot areas for the atmospheric key control zone policy are assigned a value of 1, while non-pilot regions are assigned a value of 0. This binary indicator reflects the policy-induced climate transition pressure in different regions.

4.1.4. Controlled Variables

To account for the potential influence of other factors on the economic well-being of rural households, we include a set of control variables drawn from the existing literature. These variables are selected from both the individual and household dimensions of rural households [38], totaling seven variables in all.

A detailed description of all variables is provided in

Table 1. Variable definition table.

Type	Variable	Definition & Measurement
Explained Variables Welfare	lnincome	Total annual household income ($), in logarithms
	lnpce	Total annual household consumption ($), in logarithms
	asset	Household net worth ($), in logarithms
	lnsav	Cash and savings ($), in logarithms
	happiness	Score of 0 represents the lowest and 10 represents the highest
	trust	How much trust in your neighbors (0 = very distrustful, 10 = very trustful)
	gift	Amount of annual expenditure on favors and gifts ($)
	relation	Score of 0 represents the lowest and 10 represents the highest
Explanatory Variables ETC	energy_ratio	Ratio of annual energy expenditure to household income
	cooking_fuel	Primary cooking fuel source (1 = Firewood, 2 = Coal, 3 = LPG, 4 = Natural gas, 5 = Solar energy, 6 = Electricity)
	cooking_water	Main water source for cooking (1 = River/lake, 2 = Well, 3 = Tap, 4 = Bottled/purified, 5 = Rain, 6 = Cistern, 7 = Spring)
	car	Ownership of a car (1 = Yes, 0 = No)
	power	Household electrification status (1 = Yes, 0 = No)
Moderating Variables	ETC_Climate	Climate physical risk index (number of extreme weather days divided by 360)
	ETC_Policy	Pilot area of key atmospheric control policy (1 = Yes, 0 = No)
Controlled Variables	gender	Gender (1 = Male, 0 = Female)
	age	Actual age (years)
	mar	Marital status (1 = Married, 0 = Unmarried)
	edu	Education level (0 = Illiterate, 1 = Primary, 2 = Junior high, 3 = High school, 4 = College, 5 = Bachelor, 6 = Master, 7 = Doctorate)
	health	Very healthy = 1, Very healthy = 2, Fairly healthy = 3, Average = 4, Unhealthy = 5
	employ	Employment status (1 = Not in labor force, 2 = Unemployed, 3 = Employed)
	income_per	Per capita household net income (RMB)
	size	Actual household size (number of persons)
	med	Annual household medical expenditure (RMB)
	houseasset	Value of household real estate assets (RMB)
	finasset	Value of household financial assets (RMB)
	savings	Total household savings and cash deposits (RMB)
	leisure	Household expenditure on leisure (RMB)
	insurance	Annual expenditure on commercial insurance (RMB)

.

4.2. Model Specification

4.2.1. Variable Measurement Methods

This study adopts the entropy weighting method to construct two core indices: energy transition consumption and economic well-being. As an objective weighting technique, the entropy method assigns indicator weights based on the intrinsic variability of the data, thereby minimizing subjective interference and enhancing the objectivity of the evaluation results. The entropy method operates by quantitatively analyzing the degree of dispersion or variability within each indicator. Specifically, it first computes the entropy weight of each indicator, which reflects the amount of information it conveys. Indicators with greater variability tend to receive lower entropy values, suggesting they contribute more unique and informative content to the evaluation system. Based on these computed entropy values, the initial weights are then adjusted to form a rational and objective weighting scheme, which serves as the foundation for subsequent comprehensive assessments.

The calculation process of the entropy method is as follows:

• Data standardization

In multi-indicator evaluation, the initial indicator variables have different units and orders of magnitude, which can affect the evaluation results. Therefore, it is necessary to standardize the initial indicator variables first. This paper uses the z-score standardization method to process the indicator variables. Here, $x_{ij}$ is the raw value of the $i$-th sample on the $j$-th indicator; ${\sigma }_j$ is the standard deviation of the $j$-th indicator; ${\mu }_j$ is the mean of the $j$-th indicator; $z_{ij}$ is the standardized value. In the robustness test, entropy weight method is used to determine the objective weights combined with the TOPSIS method, and range standardization is applied to eliminate the impact of different units. In the formula, $r_{ij}$ is the standardized indicator value.

z-score standardization:

$$z_{ij}={\displaystyle \frac{x_{ij}-{\mu }_j}{{\sigma }_j}}$$

(1a)

Extreme variance normalization:

$$r_{ij}={\displaystyle \frac{x_{ij}-\min \left(x_j\right)}{\max \left(x_j\right)-\min \left(x_j\right)}}$$

(1b)

$$r_{ij}={\displaystyle \frac{\max \left(x_j\right)-x_{ij}}{\max \left(x_j\right)-\min \left(x_j\right)}}$$

(1c)

2.

Calculation of indicators proportion

Further calculate the proportion of each indicator in the total data. $p_{ij}$ is the proportion of the $i$-th sample on the $j$-th indicator; $n$ is the number of samples.

$$p_{ij}={\displaystyle \frac{z_{ij}}{{\displaystyle {\sum }_{i=1}^n{z}_{ij}}}}$$

(2)

3.

Calculate the information entropy

The information entropy is used to measure the amount of information in each indicator. $e_j$ is the information entropy of the $j$-th indicator; $k={\displaystyle \frac{1}{\ln \left(n\right)}}$ is a constant to ensure that the entropy value is between 0–1.

$$e_j=-k{\displaystyle {\sum }_{i=1}^n{p}_{ij}\ln \left(p_{ij}\right)}$$

(3)

4.

Calculate the weights

The larger the information entropy is, the less informative the corresponding indicator is, and the smaller its weight is; conversely, the smaller the information entropy is, the larger the weight is. $w_j$ is the weight of the $j$-th indicator; $m$ is the total number of indicators.

$$w_j={\displaystyle \frac{1-e_j}{{\displaystyle {\sum }_{j=1}^m\left(1-e_j\right)}}}$$

(4)

5.

Construct the weighted normalized matrix and determine the ideal solution

Match the normalized data with the corresponding weights and obtain the weighted decision matrix $V=\left(v_{ij}\right)$; determine the positive ideal solution and the negative ideal solution, respectively, with the maximum value of each column as the positive ideal solution and the minimum value as the negative ideal solution.

$$\begin{gathered} v_{ij}=w_j\times r_{ij} \\ {v}_j^{+}=\underset{i}{\max } v_{ij} v_j^{-}=\underset{i}{\min } v_{ij} \end{gathered}$$

(5)

6.

Calculate the distance to the ideal solution and obtain the composite score

Calculate the Euclidean distance of each sample to the positive ideal solution and the negative ideal solution and finally get the relative closeness of each sample $C_i$; $C_i$ ∈ [0, 1], the closer to 1, the more superior the sample.

$$\begin{gathered} S_i^{+}=\sqrt{{\displaystyle {\sum }_{j=1}^m{\left(v_{ij}-v_j^{+}\right)}^2}} S_i^{-}=\sqrt{{\displaystyle {\sum }_{j=1}^m{\left(v_{ij}-v_j^{-}\right)}^2}} \\ {C}_i={\displaystyle \frac{s_i^{-}}{s_i^{+}+s_i^{-}}} \end{gathered}$$

(6)

4.2.2. Basic Model Setting

In order to study the impact of energy transition consumption on the economic well-being of rural households, the empirical model to be constructed in this paper is as follows:

$$Welfar{e}_{ijt}=\alpha +\beta ET{C}_{ijt}+\gamma {\displaystyle \sum Contro{l}_{ijt}+{\mu }_{it}}$$

(7)

$$Welfar{e}_{ijt}=d_0+d_{1}ET{C}_{ijt}+d_{2}Climat{e}_{ijt}+d_{3}ETC\_Climat{e}_{ijt}+\gamma {\displaystyle \sum Contro{l}_{ijt}+{\mu }_{it}}$$

(8)

$$Welfar{e}_{ijt}=f_0+f_{1}ET{C}_{ijt}+f_{2}Polic{y}_{jt}+f_{3}ETC\_Polic{y}_{jt}+\gamma {\displaystyle \sum Contro{l}_{ijt}+{\mu }_{it}}$$

(9)

$Welfare$ denotes the level of economic well-being of the explanatory variable rural households; $ETC$ denotes the degree of consumption of the explanatory variable rural households for energy transition; $ETC\_Climate$ denotes the cross-multiplier of the physical climate risk with the energy transition indicator; $ETC\_Policy$ denotes the cross-multiplier of the climate transition risk with the energy transition indicator; $Control$ denotes the control variable; $i$, $j$, and $t$ are the individual, region, and year, respectively; $\mu$ is a random perturbation term.

4.3. Data Sources and Descriptive Statistics

The primary data utilized in this study are drawn from the China Family Panel Studies (CFPS), conducted by the China Center for Social Science Surveys at Peking University. The CFPS provides comprehensive information across multiple dimensions, including household demographics, economic conditions, and energy consumption, offering a robust foundation for analyzing the relationship between energy transition consumption and the economic well-being of rural households.

To meet the needs of this research, the raw data were processed as follows. First, key variables were extracted from both the household economic database and the individual questionnaire database and then horizontally merged based on household and individual identifiers. The data were subsequently cleaned and organized using Stata 17.0 software. Observations with missing or invalid values were removed, and continuous variables were truncated at the 1st and 99th percentiles. A panel dataset was then constructed. Additional data were obtained from the websites of the National Bureau of Statistics and the National Meteorological Center and official publications of the State Council. Descriptive statistics for all variables are presented in

Table 2. Descriptive statistics for variables.

Variable	N	Mean	Std. Dew.	Min	Max
Welfare	21,186	0.478	0.100	0.097	0.725
ETC	21,186	0.427	0.089	0.062	0.984
age	21,186	51.638	13.842	10	94
gender	21,185	0.592	0.491	0	1
mar	20,898	0.865	0.342	0	1
size	21,186	2.924	1.404	1	11
edu	21,012	1.406	1.380	0	9
income_per	21,186	16,331.368	39,732.768	12	4,168,000
houseasset	21,186	240,714.920	770,049.110	0	50,250,000
finasset	21,186	36,691.203	132,948.275	0	12,000,000
savings	21,186	29,353.957	81,330.734	0	3,000,000
leisure	21,186	4111.178	8207.684	0	256,000
med	21,186	5197.228	14,909.779	0	740,000
insurance	21,186	1107.756	3987.194	0	100,000
employ	21,149	3.130	1.271	1	5
Policy	20,983	2.680	0.730	1	3
Climate	21,186	2.235	0.445	0.891	3.513

.

5. Empirical Tests

5.1. Benchmark Regression Results

This study employs panel data at the farm household level and selects between fixed-effects and random-effects models based on the Hausman test. The test results indicate that the fixed-effects model yields more consistent and efficient estimates, as evidenced by a statistically significant p-value. Accordingly, the fixed-effects model is adopted as the primary estimation strategy. To further improve estimation accuracy, both year fixed effects and district fixed effects are incorporated, thereby accounting for potential confounding influences from time trends and regional development disparities on farm household economic well-being.

Table 3. Benchmark regression results.

Variable	Model (1)	Model (2)	Model (3)
	Welfare	Welfare	Welfare
ETC	0.138 ***	0.112 ***	0.080 ***
	(0.007)	(0.007)	(0.007)
age		0.000 **	0.000 ***
		(0.000)	(0.000)
edu		0.003 ***	0.002 ***
		(0.000)	(0.000)
gender		0.005 ***	0.003 **
		(0.001)	(0.001)
mar		0.022 ***	0.018 ***
		(0.002)	(0.002)
employ		0.004 ***	0.003 ***
		(0.001)	(0.001)
income_per		0.000 ***	0.000 ***
		(0.000)	(0.000)
health		−0.010 ***	−0.009 ***
		(0.001)	(0.000)
size			0.003 ***
			(0.000)
med			−0.000
			(0.000)
houseasset			0.000
			(0.000)
finasset			−0.000 **
			(0.000)
savings			0.000 ***
			(0.000)
leisure			0.000 ***
			(0.000)
insurance			0.000 **
			(0.000)
District fixed effects	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes
N	21,186	20,543	20,543
R2	0.197	0.200	0.227

Note: The values in parentheses are robust standard errors, and *, **, *** represents significance at the levels of 10%, 5%, 1%, respectively.

presents the regression results. In Model (1), only energy transition consumption is included as the explanatory variable to assess its direct impact on economic well-being. The results show that energy transition consumption has a significantly positive effect at the 1% level. Models (2) and (3) sequentially introduce individual-level and household-level control variables, while also accounting for district and year fixed effects. Across all specifications, the coefficient on energy transition consumption remains positive and statistically significant at the 1% level, though its magnitude adjusts with the inclusion of control variables. These findings confirm that energy transition consumption significantly improves the economic well-being of rural households.

5.2. Endogeneity Analysis and Robustness Tests

5.2.1. Endogeneity Analysis: Instrumental Variable Approach

In the baseline regression analysis, the impact of energy consumption transition on the economic well-being of rural households may be endogenous, mainly in two aspects: firstly, the economic well-being of rural households, as a multidimensional and comprehensive indicator, is affected by a number of factors, and although this paper has controlled for a series of variables at the individual and regional levels, there may still be an omitted variable bias; and secondly, the increase in the level of the economic well-being of rural households may itself promote the optimization of clean energy demand and energy structure optimization, i.e., there may be a bidirectional causal relationship between energy consumption transition and economic well-being. To mitigate the endogeneity bias, this paper introduces the instrumental variable method for endogeneity analysis. Specifically, this paper selects the mean value of energy consumption transition in the province other than the county as an instrumental variable ($ETC\_avg$), which has strong spatial correlation with the level of local energy transition, but it does not have a direct impact on the economic well-being of local rural households, and it can satisfy the exogeneity requirement of instrumental variables. Based on this, this paper uses the two-stage least squares (2SLS) method for estimation, and the regression results are detailed in

Table 4. Instrumental variable test results.

Variable	Stage 1	Stage 2
	ETC	Welfare
ETC_avg	0.797 ***
	(0.027)
ETC		0.212 ***
		(0.032)
Controlled variables	Yes	Yes
Province fixed effects	Yes	Yes
Year fixed effects	Yes	Yes
N	20,543	20,543
R2	0.262	0.282

Note: The values in parentheses are robust standard errors, and *, **, *** represents significance at the levels of 10%, 5%, 1%, respectively.

.

The first-stage regression results confirm the strength and validity of the instrument, indicating no weak instrument problem. As reported in Table 4, the second-stage results show that the effect of energy transition consumption on farm household economic well-being remains significantly positive, consistent with the baseline regression. These findings confirm the robustness and credibility of the core results.

5.2.2. Robustness Tests: Replacement of Explained Variables

The entropy weight–TOPSIS method is an innovative improvement of the traditional TOPSIS method, and its core lies in the fact that the entropy weight method is applied to determine the weights of the indicators, and then the TOPSIS method is used to rank these weighted indicators in order to obtain an optimized evaluation score. Therefore, this paper adopts the entropy weight–TOPSIS method to determine the optimal solution and the worst solution of the indicators of economic well-being of rural households and further calculates the relative closeness between each solution and the optimal solution, so as to derive the index of economic well-being of rural households. This is used to quantitatively assess the level of economic well-being of rural households ($Welfare\_1$).

Table 5. Robustness test results.

Variable	Model (1)	Model (2)	Model (3)	Model (4)
	Welfare_1	Welfare	Welfare	Welfare
ETC_1	0.350 ***
	(0.003)
ETC		0.011 ***
		(0.002)
ETC			0.072 ***
			(0.008)
ETC				0.052 ***
				(0.008)
Controlled variables	Yes	Yes	Yes	Yes
District fixed effects	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes
N	18,292	20,693	17,116	17,116
R2	0.529	0.226	0.238	0.261

Note: The values in parentheses are robust standard errors, and *, **, *** represents significance at the levels of 10%, 5%, 1%, respectively.

yields that the effect of energy transition consumption on the economic well-being of rural households remains positive and significant at the 1% level, and the signs are all as expected. This indicates that the conclusions drawn from the baseline regression are robust and the variable measures are less likely to cause chance significance. Under this methodology, energy transition consumption still has a significant enhancing effect on the economic resilience of rural households. Thus, the research results are highly credible.

5.2.3. Robustness Tests: Replacement of Core Explanatory Variables

The equal weight method is used to remeasure energy transition consumption ($ETC\_1$). The results of the analysis in Table 5 show that the effect of energy transition consumption on the economic resilience of rural households remains positive and significant at the 1% level. This indicates that energy transition consumption has a significant positive effect on enhancing the economic resilience of rural households under the equal weight method of measurement. Specifically, by promoting energy transition and improving the quality of energy supply, energy transition consumption significantly enhances the level of economic well-being of rural households. As a result, the research results are robust to a certain extent, further validating the important role of energy transition consumption in promoting the economic well-being of rural households.

5.2.4. Culling Samples and Shrinking Tails

The paper also employs two methods to robustly test the core findings. First, samples younger than 18 or older than 65 are excluded to further control for the possible effect of sample structure on the regression results (Model 3). This treatment helps to exclude the interference of groups that are not yet involved in household decision-making or have withdrawn from productive activities, ensuring that the regression results are more representative. The results remain robust after re-estimation. Second, the key continuous variables are subjected to tailing to reduce the interference of extreme values in the regression results (Model 4). The values of the core explanatory and control variables are taken at the 1 and 99% quantiles, and the regressions are rerun on this basis by shrinking the tails. The results, as shown in Table 5, indicate that the positive effect of energy transition consumption on the economic well-being of rural households remains significant.

5.3. Further Analysis

5.3.1. Moderating Effects Analysis

To explore the mechanism through which energy transition consumption influences the economic well-being of rural households, this paper incorporates climate risk as a moderating variable in the regression analysis. Specifically, Model (1) includes a variable representing climate physical risk, along with its interaction term with energy transition consumption. As shown in

Table 6. Results of moderating effects analysis.

Variable	Model (1)	Model (2)
	Welfare	Welfare
ETC	0.057 ***	0.079 ***
	(0.011)	(0.014)
Climate	0.000
	(0.000)
ETC_Climate	−0.066 *
	(0.038)
Policy		0.014 ***
		(0.002)
ETC_Policy		−0.021 ***
		(0.006)
Controlled variables	Yes	Yes
Province fixed effects	Yes	Yes
Year fixed effects	Yes	Yes
N	20,729	15,357
R2	0.198	0.250

Note: The values in parentheses are robust standard errors, and *, **, *** represents significance at the levels of 10%, 5%, 1%, respectively.

, the coefficient of the interaction term is significantly negative, indicating that the positive effect of energy transition consumption on the economic well-being of rural households is notably weakened in regions with high climate physical risk. This may be attributed to the disruptions caused by extreme weather events to agricultural production and infrastructure, which undermine farmers’ ability to secure stable economic returns. These results confirm that climate physical risk dampens the effectiveness of energy transition consumption in improving the economic well-being of rural households.

Model (2) further introduces a proxy variable for climate transition risk and its interaction term with energy transition consumption. The regression results in Table 6 indicate that the coefficient of the interaction term is also significantly negative at the 1% level. One possible explanation is that climate transition risks increase the uncertainty surrounding traditional energy supplies, exposing farmers to short-term energy shortages and rising costs, which, in turn, limits the capacity of energy transition to enhance economic well-being. Moreover, in the process of policy implementation, some local governments may adopt short-term strategies—prioritizing environmental targets over actual rural energy needs—which reduces both the equity and sustainability of such policies. These findings suggest that climate transition risks, similar to physical risks, can significantly attenuate the economic benefits of energy transition consumption.

5.3.2. Heterogeneity Analysis

To further examine the impact of energy transition consumption on the economic well-being of rural households and to identify potential heterogeneity across different groups, this paper conducts subgroup regression analyses based on two dimensions: city classification and regional distribution. The specific grouping criteria are as follows: urban development level (first- and second-tier cities vs. third-tier and lower-tier cities) and geographic location (eastern, central, western, and northeastern regions). The regression results are presented in

Table 7. Results of heterogeneity analysis.

Variable	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)	Model (6)	Model (7)	Model (8)
	First- and Second-Level	Third-Tier and Lower-Level	East	Middle	West and Northeast	Low-Income	Middle- Income	High-Income
ETC	0.051 *	0.049 ***	0.084 ***	0.043 **	0.040 ***	0.094 ***	0.018	0.038 *
	(0.027)	(0.012)	(0.022)	(0.021)	(0.015)	(0.026)	(0.025)	(0.020)
Controlled variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
District fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	3062	17,481	5044	5522	10,073	6817	7001	6761
R2	0.163	0.166	0.213	0.163	0.256	0.210	0.161	0.244

Note: The values in parentheses are robust standard errors, and *, **, *** represents significance at the levels of 10%, 5%, 1%, respectively.

.

• Heterogeneity Analysis by City Size

The results based on city size indicate that energy transition consumption has a significant positive impact on the economic welfare of rural households in different city sizes. Specifically, the coefficient is 0.051 for Tier 1 and 2 cities and 0.049 for Tier 3 and below cities. This indicates that energy transition consumption plays a positive role in improving the welfare of rural households, irrespective of city size. Tier 1 and 2 cities with more advanced green energy infrastructure, higher public service capacity, and more financial and policy support may have provided better clean energy technologies, higher energy efficiency, and a wider range of modern energy concepts to rural households. In third-tier and lower-tier cities, although infrastructure and institutional support are relatively weaker, clean energy penetration is increasing, and to a certain extent, farmers are also being pushed to adopt clean energy consumption, thereby improving their overall economic welfare.

2.

Heterogeneity Analysis by Region

Further regressions based on geographic regions (eastern, central, western, and northeastern China) reveal that energy transition consumption has a consistently positive and significant effect on the economic well-being of rural households. In the eastern region, the coefficient is 0.084 and significant at the 1% level, indicating that in economically developed areas with well-established energy infrastructure, the benefits of energy transition consumption are more pronounced. In the central and the western and northeastern regions, the coefficients are 0.043 and 0.040, respectively. This demonstrates that even in less developed regions with relatively weaker energy infrastructure, energy transition policies still play an important role in enhancing the economic well-being of rural households.

3.

Heterogeneity Analysis by Income

Further, farm household income was categorized into low-, middle-, and high-income groups from a micro perspective, and fixed-effects regressions were conducted separately. The results show that energy consumption transition has a significant positive effect on the economic well-being of low-income and high-income rural households, indicating that energy consumption transition improves the quality of life and well-being of low-income and high-income groups; however, in the middle-income group, the regression coefficients of energy consumption transition are not significant, indicating that this group is relatively limited in the extent of the impact. This may be due to the fact that middle-income farmers neither have the capacity to pay of the high-income group nor benefit directly from policy subsidies or improved access to basic energy like the low-income group, resulting in a relatively weak marginal effect of the energy consumption transition on their well-being.

6. Conclusions

This paper investigates the relationship between energy transition consumption and the economic well-being of rural households and explores the underlying mechanisms. The empirical results indicate that energy transition consumption significantly enhances rural households’ economic well-being, with the effect being statistically significant at the 1% level. However, this positive impact is significantly moderated by both physical and transitional climate risks. Specifically, physical climate risks disrupt agricultural production and rural infrastructure, thereby increasing uncertainty in energy supply and reducing expected household income. In contrast, climate transition risks—arising from policy shifts—mainly reflect the conflicting interests among policy actors and the short-term behavior of local governments, which together hinder the effective realization of energy transition benefits at the household level.

Based on the empirical results, this paper puts forward the following three suggestions: first, strengthen infrastructure and enhance rural climate resilience. Local governments should prioritize infrastructure investment in high-risk areas, especially districts and counties that are frequently affected by floods and droughts. Build climate-resilient microgrids in remote areas to ensure stable energy supply in the event of extreme events. Establish localized agrometeorological early warning platforms linked to agricultural service stations at the township level to enable farmers to receive early warnings and guidance through mobile applications. Second, build a differentiated climate policy coordination framework. Regions should set phased climate targets based on economic capacity and resource conditions. Ensure horizontal coordination and vertical consistency. Synergize multi-province response to climate disasters, such as typhoons and floods, and share early warning information and rescue resources. Coordinated by the central government, regular cross-provincial consultation meetings should be held to resolve climate policy conflicts. Furthermore, the government should actively support the development of distributed renewable energy systems in rural areas and encourage families to establish their own zero-carbon units. Integrated projects such as “photovoltaic plus” should be utilized to address issues such as environmental protection and energy supply and to promote the sustainable development of rural economies and societies. Third, strengthen the economic security and technical support systems to facilitate farmers’ participation in the energy transition. Introduce climate insurance linked to agriculture and establish local reserves for climate disaster compensation, especially for low- and middle-income and climate-vulnerable households. Provide technical support toolkits and village-level training for farmers on clean energy system maintenance, financing, and risk response. Additionally, the government should focus on fostering farmers’ self-efficacy, strengthening social trust, and enhancing their perceived social status, which are essential for improving long-term economic well-being.

7. Limitations and Future Research Directions

Although this paper provides a systematic empirical analysis of the impact of energy transition consumption on rural households’ economic well-being and its underlying mechanisms, several areas remain open for further exploration.

First, in terms of theoretical construction, the current study lacks a detailed explanation of the micro-level mechanisms through which energy transition consumption affects household well-being. While the paper constructs an index based on energy affordability, cleanliness, and accessibility and discusses its impact from the perspective of climate regulation, it falls short in providing a systematic explanation from the household behavioral perspective. Future research could incorporate insights from behavioral economics to better explain individual heterogeneity in farmers’ energy transition decisions and uncover the underlying channels affecting economic well-being.

Second, regarding model specification, the paper employs a linear fixed-effects model to estimate the overall effect. However, it does not fully consider potential nonlinear or threshold effects in the relationship. In reality, energy spending that is too low may fail to meet basic needs, while excessive spending may crowd out other essential expenditures, leading to diminishing or even negative marginal utility. Future studies could apply segmented regression, threshold models, or non-parametric methods to capture the marginal effect dynamics at different energy consumption levels.

Finally, while this paper includes micro-level heterogeneity analysis based on household income, future work should further explore heterogeneity in agricultural production types, levels of digital energy literacy, and other household characteristics that may influence the outcomes of energy transition in more complex ways.

In sum, this study serves as an initial exploration of the relationship between energy transition and farm household well-being, but there remains significant room for theoretical refinement and methodological innovation. Future research should adopt interdisciplinary perspectives, utilize higher-quality micro-level data, and employ more explanatory theoretical models to deepen and strengthen this line of inquiry.