Prospect of water energy environment nexus under energy and climate change scenarios (case study: Urmia Lake Basin)

The water energy environment (WEE) nexus models have become indispensable for the integrated natural resources management under the growing regional and global risks and insecurities in pursuit of sustainable development. A new framework has been developed based on bottom-up energy system model and the related greenhouse gas emissions which aimed to predict and depict an apparent WEE nexus outlook for residential, electric power, industry, and agriculture sectors under various scenarios. Bottom-up nexus modeling has been done by Long Energy Alternative Planning software tool for the mentioned sectors from 2016 to 2040. The Urmia Lake basin covering 52,000 Km(2) as a vulnerable region to climate change located in the northwest of Iran is selected as a case study in this paper. The UL basin's demographic and economic data were used as energy demand drivers. Discussion of the results based on the detailed energy and emission analysis under different scenarios showed the most energy saving and environmental pollutants abatement potential equal to 27.76 million barrels of oil equivalent and 11.3 million metric tons of carbon dioxide equivalents under the Integrated Policy (IP) scenario up to 2040. Sensitivity analysis of total energy demand to socioeconomic changes shows mean increases of 3% and 2% to each unit increase in the population and gross domestic product. The cost-benefit analysis for the IP scenario indicates more net present values if the interest rate remains less than 8%. [GRAPHICS]

Automated summary

The study focuses on the development and application of water energy environment (WEE) nexus models in the Urmia Lake basin in Iran, assessing energy demand drivers and potential energy saving and environmental pollutants abatement strategies under different scenarios. Results indicate significant potential for energy saving and emissions reduction in the Integrated Policy (IP) scenario, with sensitivity analysis showing the impact of population and GDP changes on total energy demand.