Geospatial multi-criteria analysis for identifying high priority clean energy investment opportuniti...

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Geospatial multi-criteria analysis for identifying high priority clean energy investment opportunities: "
Shiraishi, KShirley, RGKammen, DM

Document Type	:	AL
Record Number	:	899951
Doc. No	:	LA153814hh
Title & Author	:	Geospatial multi-criteria analysis for identifying high priority clean energy investment opportunities: [Article]. A case study on land-use conflict in Bangladesh\ Shiraishi, KShirley, RGKammen, DM
Date	:	2019
Title of Periodical	:	UC Berkeley
Abstract	:	© 2018 Elsevier Ltd Bangladesh is a globally important emerging economy with rapidly increasing energy demand. The Bangladeshi government's primary capacity expansion plan is to install 13.3 GW of new coal by 2021, including the 1.3 GW Rampal coal power plant to be developed in the Sundarbans. Inadequate geospatial and economic information on clean energy investment opportunities are often a significant barrier for policy makers. Our study helps fill this gap by applying a new method to assess energy investment opportunities, with focus on understanding land-use conflicts, particularly important in this context as Bangladesh is constrained on land for agriculture, human settlements, and ecological preservation. By extending a geospatial multi-criteria analysis model (MapRE) we analyze the cost of various renewable energy generation technologies based on resource availability and key siting criteria such as proximity to transmission and exclusion from steep slopes, dense settlements or ecologically sensitive areas. We find there is more utility-scale solar potential than previously estimated, which can be developed at lower costs than coal power and with minimal cropland tradeoff. We also find significant potential for decentralized roof-top solar in commercial and residential areas. Even with a conservative land use program that reserves maximum land for agriculture and human settlement, there is more renewable energy capacity than needed to support Bangladeshi growth. This study provides critical and timely information for capacity expansion planning in South Asia and demonstrates the use of geospatial models to support decision-making in data-limited contexts.