How do photovoltaic poverty alleviation projects relieve household energy poverty? Evidence from China

Gospel or curse: the impact of religious beliefs on energy poverty in rural China

“…Households situated on plains exhibit a reduced propensity to experience energy poverty. This is attributed to well-developed rural infrastructure and flat terrain, which create conducive conditions for establishing energy networks, such as power grids and photovoltaic systems (Li et al 2023b;Lin and Zhao 2021).…”

Can Solar Photovoltaic Poverty Alleviation Policies Reduce Carbon Emissions and Increase Income in China?

“…In addition, the PVPA policy has offered a sustainable solution to ameliorating rural energy poverty by providing electricity access to nearly 0.63 million impoverished households between 2014 and 2020 (equivalent to 2.6% of the nation’s indigent households). Notably, this effort has bolstered the utilization of clean energy by impoverished households, yielding a remarkable 23% increase, thereby catalyzing a profound transformation in rural energy poverty. , Evidently, the PVPA policy has resulted in a significant 6.32% reduction in energy poverty for rural households while concurrently catalyzing a transition toward cleaner household energy usage . Overall, the poverty-alleviating and emission-reducing effects of China’s PVPA policy are positive and sustainable. , The PVPA policy effectively mobilized marginalized communities to actively engage in labor, fostering an intrinsic momentum for sustainable development.…”

Public acceptance of residential photovoltaic installation: A case study in China

“…Statistical yearbook data of Hebei, Shandong and Henan provinces indicate that their population structure and energy structure ratios [ [79] , [80] , [81] ] are similar to those in the national statistical yearbook of 2021 [ 78 ], Hebei, Shandong, and Henan accounted for 84.6 % of the country's residential PV projects subsidized by national finance in 2021 (40.2 %, 32.3 %, and 12.1 %, respectively) [ 58 , 69 ], so data from these provinces would be more representative ( Appendix S2 shows detailed population and energy structure). These provinces provided a typical sample for the analysis of purchase deciding factors and intention among different groups.…”