Session 2C: Renewable Energy and Green Financing  (Auditorium 3)

Firm investment in renewable energy: An empirical evidence from the People’s Republic of China

Dina Azhgaliyeva and Hai Le

Green peer-to-peer lending: New opportunities for governmental support and regulation

Dina Azhgaliyeva and Hai Le

          Lending through peer-to-peer lending platforms is growing fast along with green peer-to-peer lending. This paper analysis the benefits and risks arising from green peer-to-peer lending platforms. Green peer-to-peer lending can help to accumulate private finance for deployment of renewable energy, energy efficiency technologies and other environmentally friendly projects. Green private finance is necessary to meet the growing global energy demand and limit the temperature rise to 1.5 degrees Celsius by 2100 in order to prevent disastrous climate change. The additional benefits and risks arising from green fintech comparing from generic fintech raise the need to distinguish green fintech in financial regulation. This paper provides a broad overview of green peer-to-peer lending, with some examples, of how these emerging technologies can provide solutions for the promotion of renewable energy and energy efficiency and how governments and regulators can promote green peer-to-peer lending.

JEL Code/s: Q28, Q42, Q48, G23

Measuring energy poverty in Kazakhstan: New evidence from a semiparametric model

Dina Azhgaliyeva, Sandugash Juatova, Aiymgul Kerimray  and  Zhanna Kapsalyamova

          Measurement of energy poverty commonly applies a 10 percent indicator approach based on the disposable income level or total expenditure on consumption. Such a method has shortcomings, as it is rather ad-hoc in terms of specifying the threshold of 10 percent above which households are treated as energy poor. Using a semiparametric regression to estimate the varying income elasticity of energy demand we identify the threshold points to identify the energy-poor households. From the literature income elasticity of demand tends to decline as households get richer and we exploit that to find corresponding inflection/threshold points. Using household budget survey data from Kazakhstan for 2012, 2014, and 2016, we use our proposed method to estimate energy poverty in Kazakhstan. Our analysis reveals that the size of the total energy poverty in Kazakhstan is below 7%, while poverty across different fuels shows a larger variation. Electricity poverty is below 7.5 percent, natural gas is below 37 percent and coal poverty are below 20 percent. 

JEL Code/s: Q40, Q49, I32