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The main contradictions in Chinese society have changed as China enters a new era, and the people's pursuit of the better life has become more intense. This study explores how energy poverty can be addressed from a digital perspective in the context of the booming digital economy. This study analyzed the impact of mobile phone use on energy poverty by constructing an OLS model using micro data from Tibetan farming and pastoral areas. We also conduct tests for endogeneity, robustness, heterogeneity, and influence mechanisms. The results indicate that mobile phone use has a significant negative impact on energy poverty, and the results remain robust after instrumental-variable regression. In terms of economic utility, mobile phone use reduces energy poverty by increasing household consumption or off-farm income. In terms of social utility, mobile phone use can also alleviate energy poverty by raising social capital. Heterogeneity analysis shows that mobile phone use contributes to energy poverty alleviation in a way that benefits the poor and the less educated. The authors investigate the impact of mobile phone use on Energy Poverty, a topic that has previously been little addressed in academia. The findings of this paper enrich the literature on the digital technology and energy poverty and provide a new perspective for alleviating energy poverty in rural areas and improving farmers' well-being. Finally, this paper offers some policy recommendations. • The use of mobile phones by farmers and herdsmen can alleviate energy poverty. • mobile phone use contributes to energy poverty alleviation in ways that benefit the poor and the less educated. • The social and economic utility brought by the use of mobile phones can help farmers and herdsmen alleviate energy poverty. [ABSTRACT FROM AUTHOR]

Energy poverty is the inability to meet necessary household energy needs and has threatened human well-being and exacerbated climate change. However, there has been a lack of systematic analysis regarding the driving factors of energy poverty and their interaction mechanisms, which is crucial for achieving effective energy poverty reduction. This paper proposes a novel integrated technique composed of the Backpropagation Neural Network (BPNN) and Weighted Influence Non-linear Gauge System (WINGS) method to identify the most important driving factors of energy poverty and understand their interaction mechanism by using household-level survey data in China from 2016, 2018, and 2020. The findings reveal that per capita household income is the most important cause-driving factor. Household heads' education attainment and policy support, such as government subsidy and housing provident fund, are also significant causes of energy poverty. Factors, such as household central heating system usage and house ownership, are in the downstream of the interaction mechanism chain to be considered as the effect-driving factors. Finally, a robustness test is conducted to validate the effectiveness of the proposed method and the obtained findings, which could assist energy-related decision-makers with the most significant drivers for reducing energy poverty. [ABSTRACT FROM AUTHOR]

Energy poverty occurs when a household cannot fulfill its energy needs. Despite its widespread occurrence, Chinese cities have yet to formally recognize energy poverty as distinct from general poverty at the national level, thereby limiting the effectiveness of countermeasures. Guangzhou, a subtropical first-tier city in China, has a complex population comprising diverse migrants and locals of all ages. This age diversity greatly affects energy demand and poverty sensitivity, so analyzing age's impact on energy poverty is crucial. Thus, this paper surveyed 1072 households in 2020 to measure the extent of energy poverty. The energy poverty rate was found to be 13.85 %. Additionally, a U-shaped relationship was discovered between age and energy poverty, with a turning point at age 44. The adults in Guangzhou who are in energy poverty are not in income poverty. Higher education levels were found to impact energy consumption in households of both younger and older adults. For individuals older than 42—or 46 with a higher education level—the likelihood of experiencing energy poverty gradually decreases with age. Our findings indicate a unique pattern in which both younger and older individuals are susceptible to energy poverty in a humid subtropical climate. To achieve the UN's 2030 clean energy goals, policymakers must focus on tailored skill training, enhanced social welfare, and effective affordable housing policies to alleviate energy poverty. • We use survey datato measure Guangzhou household energy poverty. The LIHC indicator is 13.86%. • The relationship between age and energy poverty in the household is U-shaped. • Younger and older residents are vulnerable to energy poverty. • Younger and older highly educated individuals are more vulnerable to energy poverty than those without higher education. [ABSTRACT FROM AUTHOR]

Accelerating the energy transition of rural households in mountainous areas is an important step to improve energy poverty and mountain ecosystems and to help achieve carbon peaking and carbon neutrality. This paper explores the transformation of rural cooking energy in mountainous areas based on field investigation data from six villages in Chongqing. It systematically reveals the transformation process, driving factors and carbon emission changes in household cooking energy consumption in mountainous areas in the past 20 years by using mathematical statistics, multinomial logit models and the emission coefficient method. The results are as follows. (1) There has been a significant transition in the consumption of rural household cooking energy in mountainous areas. There has been a significant change in the proportion of different energy sources, including fuelwood (59.2%–1.9%), coal (16.5%–0%), gas (12.6%–15.5%), natural gas (6.8%–51.5%) and electricity (4.9%–31.1%). (2) The main driving factors of the household cooking energy transition in mountainous areas are income per capita, householder age, and householder education level. (3) The average annual household carbon emissions from rural household cooking energy decreased remarkably. The average annual household carbon emissions decreased from 6.80 t to 2.62 t in fuelwood (5.40–0.91 t), electricity (0.19–1.28 t) and natural gas (0.03–0.25 t) emissions during the study period. The contribution of this study is to investigate how the household cooking energy transition contributes to carbon peaking and carbon neutrality from a micro perspective. At the same time, this paper explores the mechanisms between the cooking energy transition and the ecological dividend of urbanization and proposes future reform initiatives for rural energy in mountainous areas to help achieve the goals of carbon peaking and carbon neutrality. [ABSTRACT FROM AUTHOR]

Energy poverty is a serious problem worldwide that negatively impacts the subjective well-being of elderly residents. This paper aims to explore this relationship. Firstly, based on 2011, 2013, 2015, and 2018 data of the China Health and Retirement Longitudinal Study (CHARLS) launched by Peking University for 125 prefecture-level cities, representing 32,490 observations, this paper effectively and multidimensionally measures the level of individual energy poverty. Secondly, a mediating model is adopted to study the causal effect between energy poverty and subjective well-being through the OLS and 2SLS methods using carefully selected instrumental variables to overcome potential endogeneity. Finally, through a variety of robustness tests, the stability of the above causal effects is verified. The results show that energy poverty negatively affects senior citizens' subjective well-being. Health, depression level, and household food expenditure level can be used as the micro mechanism between the two variables. The above factors have a greater impact on those senior citizens with low educational backgrounds, from relatively low-income families, living alone, and having an urban household registration. [ABSTRACT FROM AUTHOR]