Welcome to Xi'an Jiaotong University!

XJTU team makes progress in energy transition and shared prosperity research

December 31, 2024
  L M S

992E1F838E99E15CED08EDA0505_360839FE_F9D1.jpg

The mechanism analysis of how energy transition drives shared prosperity.

A research team led by Professor Li Cong from the School of Economics and Finance of Xi'an Jiaotong University (XJTU), in collaboration with researchers from City University of Hong Kong, the Chinese Academy of Sciences, and Stanford University, has made significant progress in studying the income distribution effects of energy transition. The key findings are as follows:

Promoting clean energy has immense potential to improve income distribution, fostering household income growth and reducing inequality. For low-income groups, the income-enhancing effects of using clean energy are even greater. The promotion of clean energy can help facilitate upward income mobility and reduce downward income movement, thus optimizing income distribution.

The income distribution effects of clean energy are primarily driven by improvements in human capital and labor supply. The use of clean energy reduces the incidence of lung diseases, decreases the number of chronic illnesses, improves health levels, and lowers household medical expenses. Additionally, clean energy usage saves time spent on fuel collection and cooking, thus reducing household labor and increasing labor supply, which in turn boosts wage income.

This research suggests that policymakers can incorporate the relationship between energy, poverty, and inequality into their decision-making processes. It provides important insights for coordinating the advancement of multiple United Nations Sustainable Development Goals (SDGs) and accelerating the realization of these goals, which promote shared prosperity.

The research was published online in the Proceedings of the National Academy of Sciences of the United States of America under the title "Energy-poverty-inequality SDGs: A large-scale household analysis and forecasting in China". Li and Ph.D. student Li Minglai from XJTU are the co-first authors.