The impact decades-long dependence on hydropower in El Nino prone Zambia is having on carbon emissions through diesel backup generation

D’Maris Coffman, Graham Sianjase, Priti Parikh and I have published a journal paper in Environmental Research Letters under the title above. For this article, we make available the primary data our team of enumerators (Johanna Mwila, Beauty Nkosha, Tapiwa Janda, Nandi Ngwenye and Mundia Kayamba) helped us collect in the above Excel file. We do this to allow for scrutiny of our findings. We will disclose more data collected and analysis once we have published our findings pertaining to the additional data. The data available is GDPR compliant.

The pdf of the article can be accessed here.

Abstract

Emissions associated with hydropower are often forgotten. Lifecycle assessments of greenhouse gas emissions emanating from hydropower must count embedded carbon, emissions from reservoir lakes and the loss of carbon sinks, as well as backup diesel generation emissions when dependence on hydropower fails to deliver energy. Using Zambia as a case study, we estimate using a bottom-up approach that the emissions associated with backup diesel generation from Zambia’s power utility ZESCO and three largest sectors of consumers were up to 27 000 tonnes of ${\text{C}}{{\text{O}}_2}$ in the worst months of drought in 2019. This is significantly higher than what a previous top-down approach would have estimated.

We worked out ZESCO’s diesel generation attributable to drought using trend analysis. We worked out the mining sector’s emissions using copper production data, on-grid electricity consumption and calculated electricity intensity to infer off-grid electricity consumption in years of drought. From our household survey we learned average duration of generator use, average capacities of generators and acquired household income and generator use data which we ran in a Tobit regression. These together with labour force survey data helped us infer the level of diesel generation by households of different income brackets. For manufacturing firms we surveyed 123 firms. We collected rich diesel generation use data covering years of drought, input this into an OLS regression to identify predictors of diesel generation use (installed capacity of generator in kVA, in litres and whether generation was in a drought year) which we then used to extrapolate implied diesel generation for the firms for which we had less rich data.

As global average temperatures and the frequency of El Niño droughts rise in hydropower dependent countries which account for a fifth of the world’s population, backup generation emissions have implications for the formulation of low carbon energy policy.

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